Overview

Dataset statistics

Number of variables 113
Number of observations 37606
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 3724
Duplicate rows (%) 9.9%
Total size in memory 32.4 MiB
Average record size in memory 904.0 B

Variable types

Numeric 16
Categorical 97

Alerts

Dataset has 3724 (9.9%) duplicate rows Duplicates
bodystyle_Coupe is highly overall correlated with drivetrain_Rear-wheel Drive High correlation
bodystyle_SUV is highly overall correlated with bodystyle_Sedan and 1 other fields High correlation
bodystyle_Sedan is highly overall correlated with bodystyle_SUV High correlation
cat_x0 is highly overall correlated with cat_x1 and 1 other fields High correlation
cat_x1 is highly overall correlated with bodystyle_SUV and 1 other fields High correlation
cat_x2 is highly overall correlated with drivetrain_Rear-wheel Drive High correlation
drivetrain_All-wheel Drive is highly overall correlated with drivetrain_Front-wheel Drive High correlation
drivetrain_Front-wheel Drive is highly overall correlated with drivetrain_All-wheel Drive and 1 other fields High correlation
drivetrain_Rear-wheel Drive is highly overall correlated with bodystyle_Coupe and 2 other fields High correlation
exterior_color_x0 is highly overall correlated with exterior_color_x2 High correlation
exterior_color_x2 is highly overall correlated with exterior_color_x0 and 1 other fields High correlation
exterior_color_x4 is highly overall correlated with exterior_color_x2 High correlation
fuel_type_Electric is highly overall correlated with fuel_type_Gasoline High correlation
fuel_type_Gasoline is highly overall correlated with fuel_type_Electric and 1 other fields High correlation
fuel_type_Hybrid is highly overall correlated with fuel_type_Gasoline High correlation
interior_color_x0 is highly overall correlated with interior_color_x1 and 1 other fields High correlation
interior_color_x1 is highly overall correlated with interior_color_x0 and 1 other fields High correlation
interior_color_x2 is highly overall correlated with interior_color_x0 and 1 other fields High correlation
interior_color_x3 is highly overall correlated with interior_color_x4 and 1 other fields High correlation
interior_color_x4 is highly overall correlated with interior_color_x3 High correlation
make_Acura is highly overall correlated with model_hashed_27 High correlation
make_Dodge is highly overall correlated with model_hashed_12 High correlation
make_Mazda is highly overall correlated with model_hashed_43 High correlation
make_Nissan is highly overall correlated with interior_color_x3 High correlation
make_RAM is highly overall correlated with model_hashed_30 High correlation
make_Subaru is highly overall correlated with model_hashed_22 High correlation
mileage is highly overall correlated with year High correlation
model_hashed_12 is highly overall correlated with make_Dodge High correlation
model_hashed_22 is highly overall correlated with make_Subaru High correlation
model_hashed_27 is highly overall correlated with make_Acura High correlation
model_hashed_30 is highly overall correlated with make_RAM High correlation
model_hashed_43 is highly overall correlated with make_Mazda High correlation
msrp is highly overall correlated with drivetrain_Front-wheel Drive High correlation
stock_type is highly overall correlated with year High correlation
year is highly overall correlated with mileage and 1 other fields High correlation
model_hashed_0 is highly imbalanced (86.6%) Imbalance
model_hashed_1 is highly imbalanced (94.0%) Imbalance
model_hashed_2 is highly imbalanced (93.0%) Imbalance
model_hashed_3 is highly imbalanced (89.3%) Imbalance
model_hashed_4 is highly imbalanced (95.2%) Imbalance
model_hashed_5 is highly imbalanced (92.1%) Imbalance
model_hashed_6 is highly imbalanced (94.7%) Imbalance
model_hashed_7 is highly imbalanced (85.6%) Imbalance
model_hashed_8 is highly imbalanced (92.7%) Imbalance
model_hashed_9 is highly imbalanced (88.4%) Imbalance
model_hashed_10 is highly imbalanced (92.1%) Imbalance
model_hashed_11 is highly imbalanced (95.5%) Imbalance
model_hashed_12 is highly imbalanced (82.9%) Imbalance
model_hashed_13 is highly imbalanced (81.8%) Imbalance
model_hashed_14 is highly imbalanced (86.9%) Imbalance
model_hashed_15 is highly imbalanced (95.6%) Imbalance
model_hashed_16 is highly imbalanced (91.2%) Imbalance
model_hashed_17 is highly imbalanced (89.5%) Imbalance
model_hashed_18 is highly imbalanced (90.7%) Imbalance
model_hashed_19 is highly imbalanced (88.3%) Imbalance
model_hashed_20 is highly imbalanced (82.4%) Imbalance
model_hashed_21 is highly imbalanced (93.1%) Imbalance
model_hashed_22 is highly imbalanced (88.0%) Imbalance
model_hashed_23 is highly imbalanced (87.9%) Imbalance
model_hashed_24 is highly imbalanced (91.9%) Imbalance
model_hashed_25 is highly imbalanced (92.6%) Imbalance
model_hashed_26 is highly imbalanced (94.4%) Imbalance
model_hashed_27 is highly imbalanced (82.4%) Imbalance
model_hashed_28 is highly imbalanced (88.5%) Imbalance
model_hashed_29 is highly imbalanced (83.5%) Imbalance
model_hashed_30 is highly imbalanced (87.7%) Imbalance
model_hashed_31 is highly imbalanced (94.8%) Imbalance
model_hashed_32 is highly imbalanced (90.2%) Imbalance
model_hashed_33 is highly imbalanced (90.6%) Imbalance
model_hashed_34 is highly imbalanced (91.4%) Imbalance
model_hashed_35 is highly imbalanced (95.1%) Imbalance
model_hashed_36 is highly imbalanced (97.0%) Imbalance
model_hashed_37 is highly imbalanced (86.7%) Imbalance
model_hashed_38 is highly imbalanced (92.3%) Imbalance
model_hashed_39 is highly imbalanced (92.1%) Imbalance
model_hashed_40 is highly imbalanced (87.6%) Imbalance
model_hashed_41 is highly imbalanced (95.2%) Imbalance
model_hashed_42 is highly imbalanced (95.2%) Imbalance
model_hashed_43 is highly imbalanced (93.1%) Imbalance
model_hashed_44 is highly imbalanced (97.1%) Imbalance
model_hashed_45 is highly imbalanced (92.1%) Imbalance
model_hashed_46 is highly imbalanced (92.0%) Imbalance
model_hashed_47 is highly imbalanced (95.0%) Imbalance
model_hashed_48 is highly imbalanced (79.0%) Imbalance
model_hashed_49 is highly imbalanced (94.7%) Imbalance
model_hashed_50 is highly imbalanced (92.4%) Imbalance
model_hashed_51 is highly imbalanced (87.2%) Imbalance
drivetrain_Rear-wheel Drive is highly imbalanced (74.8%) Imbalance
make_Acura is highly imbalanced (85.7%) Imbalance
make_Audi is highly imbalanced (79.0%) Imbalance
make_BMW is highly imbalanced (71.6%) Imbalance
make_Buick is highly imbalanced (90.4%) Imbalance
make_Cadillac is highly imbalanced (79.7%) Imbalance
make_Chevrolet is highly imbalanced (53.5%) Imbalance
make_Dodge is highly imbalanced (81.3%) Imbalance
make_Ford is highly imbalanced (55.7%) Imbalance
make_GMC is highly imbalanced (84.3%) Imbalance
make_Honda is highly imbalanced (78.1%) Imbalance
make_Hyundai is highly imbalanced (65.5%) Imbalance
make_INFINITI is highly imbalanced (88.5%) Imbalance
make_Jeep is highly imbalanced (60.0%) Imbalance
make_Kia is highly imbalanced (80.1%) Imbalance
make_Land Rover is highly imbalanced (90.3%) Imbalance
make_Lexus is highly imbalanced (85.6%) Imbalance
make_Lincoln is highly imbalanced (86.4%) Imbalance
make_Mazda is highly imbalanced (78.9%) Imbalance
make_Mercedes-Benz is highly imbalanced (65.1%) Imbalance
make_Nissan is highly imbalanced (64.7%) Imbalance
make_Porsche is highly imbalanced (95.2%) Imbalance
make_RAM is highly imbalanced (88.2%) Imbalance
make_Subaru is highly imbalanced (74.0%) Imbalance
make_Toyota is highly imbalanced (78.3%) Imbalance
make_Volkswagen is highly imbalanced (67.8%) Imbalance
make_Volvo is highly imbalanced (92.4%) Imbalance
bodystyle_Cargo Van is highly imbalanced (91.9%) Imbalance
bodystyle_Convertible is highly imbalanced (93.9%) Imbalance
bodystyle_Coupe is highly imbalanced (81.6%) Imbalance
bodystyle_Hatchback is highly imbalanced (91.8%) Imbalance
bodystyle_Minivan is highly imbalanced (98.9%) Imbalance
bodystyle_Passenger Van is highly imbalanced (96.2%) Imbalance
bodystyle_Pickup Truck is highly imbalanced (58.2%) Imbalance
bodystyle_Wagon is highly imbalanced (96.7%) Imbalance
bodystyle_nan is highly imbalanced (97.2%) Imbalance
fuel_type_Electric is highly imbalanced (78.1%) Imbalance
fuel_type_Flexible is highly imbalanced (96.5%) Imbalance
fuel_type_Gasoline is highly imbalanced (61.7%) Imbalance
fuel_type_Hybrid is highly imbalanced (77.6%) Imbalance
mileage has 1543 (4.1%) zeros Zeros

Reproduction

Analysis started 2024-05-20 05:00:29.112146
Analysis finished 2024-05-20 05:01:55.974551
Duration 1 minute and 26.86 seconds
Software version ydata-profiling vv4.7.0
Download configuration config.json

Variables

msrp
Real number (ℝ)

HIGH CORRELATION 

Distinct 22059
Distinct (%) 58.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 46394.021
Minimum 5991
Maximum 270710
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 293.9 KiB
2024-05-20T00:01:56.083084 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 5991
5-th percentile 21768.458
Q1 31356.101
median 42285.5
Q3 55868.99
95-th percentile 85523.217
Maximum 270710
Range 264719
Interquartile range (IQR) 24512.889

Descriptive statistics

Standard deviation 22072.061
Coefficient of variation (CV) 0.47575228
Kurtosis 7.1422003
Mean 46394.021
Median Absolute Deviation (MAD) 12040.5
Skewness 1.8332037
Sum 1.7446936 × 109
Variance 4.8717589 × 108
Monotonicity Not monotonic
2024-05-20T00:01:56.268687 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
34085 62
 
0.2%
54595 60
 
0.2%
36926 49
 
0.1%
35975 48
 
0.1%
33160 47
 
0.1%
28331 42
 
0.1%
32189 42
 
0.1%
48950 41
 
0.1%
33389 38
 
0.1%
32005 36
 
0.1%
Other values (22049) 37141
98.8%
Value Count Frequency (%)
5991 1
< 0.1%
5995 1
< 0.1%
6000 1
< 0.1%
6188 1
< 0.1%
6649.48 1
< 0.1%
6679.57 1
< 0.1%
6688.68 1
< 0.1%
6783.84 1
< 0.1%
6888.45 1
< 0.1%
6929.44 1
< 0.1%
Value Count Frequency (%)
270710 1
< 0.1%
254160 1
< 0.1%
251160 1
< 0.1%
249320 1
< 0.1%
242742 1
< 0.1%
238805 1
< 0.1%
229950 2
< 0.1%
227565 1
< 0.1%
226420 1
< 0.1%
225550 1
< 0.1%

year
Real number (ℝ)

HIGH CORRELATION 

Distinct 47
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 2021.7396
Minimum 1961
Maximum 2025
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 293.9 KiB
2024-05-20T00:01:56.527610 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 1961
5-th percentile 2014
Q1 2021
median 2024
Q3 2024
95-th percentile 2024
Maximum 2025
Range 64
Interquartile range (IQR) 3

Descriptive statistics

Standard deviation 3.7656761
Coefficient of variation (CV) 0.0018625921
Kurtosis 13.768701
Mean 2021.7396
Median Absolute Deviation (MAD) 0
Skewness -2.7285996
Sum 76029539
Variance 14.180316
Monotonicity Not monotonic
2024-05-20T00:01:56.677757 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
Value Count Frequency (%)
2024 20160
53.6%
2021 3417
 
9.1%
2023 2967
 
7.9%
2020 1885
 
5.0%
2022 1703
 
4.5%
2019 1383
 
3.7%
2018 1185
 
3.2%
2017 936
 
2.5%
2016 783
 
2.1%
2015 659
 
1.8%
Other values (37) 2528
 
6.7%
Value Count Frequency (%)
1961 1
< 0.1%
1965 1
< 0.1%
1968 1
< 0.1%
1972 1
< 0.1%
1974 1
< 0.1%
1980 1
< 0.1%
1982 1
< 0.1%
1984 2
< 0.1%
1986 1
< 0.1%
1987 1
< 0.1%
Value Count Frequency (%)
2025 415
 
1.1%
2024 20160
53.6%
2023 2967
 
7.9%
2022 1703
 
4.5%
2021 3417
 
9.1%
2020 1885
 
5.0%
2019 1383
 
3.7%
2018 1185
 
3.2%
2017 936
 
2.5%
2016 783
 
2.1%

mileage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct 14766
Distinct (%) 39.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 22738.545
Minimum 0
Maximum 962839
Zeros 1543
Zeros (%) 4.1%
Negative 0
Negative (%) 0.0%
Memory size 293.9 KiB
2024-05-20T00:01:56.841520 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 1
Q1 6
median 25.985536
Q3 36179
95-th percentile 101801
Maximum 962839
Range 962839
Interquartile range (IQR) 36173

Descriptive statistics

Standard deviation 36902.026
Coefficient of variation (CV) 1.6228842
Kurtosis 15.311465
Mean 22738.545
Median Absolute Deviation (MAD) 25.985536
Skewness 2.341604
Sum 8.5510573 × 108
Variance 1.3617595 × 109
Monotonicity Not monotonic
2024-05-20T00:01:56.997923 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
5 2911
 
7.7%
10 2516
 
6.7%
0 1543
 
4.1%
3 1490
 
4.0%
6 1419
 
3.8%
2 1139
 
3.0%
7 947
 
2.5%
1 920
 
2.4%
11 901
 
2.4%
4 846
 
2.2%
Other values (14756) 22974
61.1%
Value Count Frequency (%)
0 1543
4.1%
0.78 1
 
< 0.1%
1 920
2.4%
1.09 2
 
< 0.1%
1.44 1
 
< 0.1%
2 1139
3.0%
2.03 1
 
< 0.1%
2.292666667 1
 
< 0.1%
2.51 1
 
< 0.1%
2.644166667 1
 
< 0.1%
Value Count Frequency (%)
962839 1
< 0.1%
440911 1
< 0.1%
324349 1
< 0.1%
317568 1
< 0.1%
310000 1
< 0.1%
304425 1
< 0.1%
270498 1
< 0.1%
268470 1
< 0.1%
265649 1
< 0.1%
259370 1
< 0.1%

stock_type
Categorical

HIGH CORRELATION 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
1.0
21022 
0.0
16584 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 1.0
3rd row 1.0
4th row 1.0
5th row 1.0

Common Values

Value Count Frequency (%)
1.0 21022
55.9%
0.0 16584
44.1%

Length

2024-05-20T00:01:57.143505 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:57.282645 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
1.0 21022
55.9%
0.0 16584
44.1%

Most occurring characters

Value Count Frequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 54190
48.0%
. 37606
33.3%
1 21022
 
18.6%

model_hashed_0
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36503 
1.0
 
886
-1.0
 
217

Length

Max length 4
Median length 3
Mean length 3.0057704
Min length 3

Characters and Unicode

Total characters 113035
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36503
97.1%
1.0 886
 
2.4%
-1.0 217
 
0.6%

Length

2024-05-20T00:01:57.454911 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:57.565002 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36503
97.1%
1.0 1103
 
2.9%

Most occurring characters

Value Count Frequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 113035
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113035
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113035
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74109
65.6%
. 37606
33.3%
1 1103
 
1.0%
- 217
 
0.2%

model_hashed_1
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37200 
1.0
 
279
-1.0
 
127

Length

Max length 4
Median length 3
Mean length 3.0033771
Min length 3

Characters and Unicode

Total characters 112945
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37200
98.9%
1.0 279
 
0.7%
-1.0 127
 
0.3%

Length

2024-05-20T00:01:57.739227 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:57.855489 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37200
98.9%
1.0 406
 
1.1%

Most occurring characters

Value Count Frequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112945
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112945
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112945
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74806
66.2%
. 37606
33.3%
1 406
 
0.4%
- 127
 
0.1%

model_hashed_2
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37121 
-1.0
 
306
1.0
 
179

Length

Max length 4
Median length 3
Mean length 3.008137
Min length 3

Characters and Unicode

Total characters 113124
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37121
98.7%
-1.0 306
 
0.8%
1.0 179
 
0.5%

Length

2024-05-20T00:01:57.980582 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.103390 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37121
98.7%
1.0 485
 
1.3%

Most occurring characters

Value Count Frequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113124
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113124
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113124
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74727
66.1%
. 37606
33.2%
1 485
 
0.4%
- 306
 
0.3%

model_hashed_3
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36799 
-1.0
 
525
1.0
 
282

Length

Max length 4
Median length 3
Mean length 3.0139605
Min length 3

Characters and Unicode

Total characters 113343
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36799
97.9%
-1.0 525
 
1.4%
1.0 282
 
0.7%

Length

2024-05-20T00:01:58.228004 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.467161 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36799
97.9%
1.0 807
 
2.1%

Most occurring characters

Value Count Frequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 113343
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113343
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113343
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74405
65.6%
. 37606
33.2%
1 807
 
0.7%
- 525
 
0.5%

model_hashed_4
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37302 
-1.0
 
165
1.0
 
139

Length

Max length 4
Median length 3
Mean length 3.0043876
Min length 3

Characters and Unicode

Total characters 112983
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37302
99.2%
-1.0 165
 
0.4%
1.0 139
 
0.4%

Length

2024-05-20T00:01:58.594294 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.711860 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37302
99.2%
1.0 304
 
0.8%

Most occurring characters

Value Count Frequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112983
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112983
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112983
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74908
66.3%
. 37606
33.3%
1 304
 
0.3%
- 165
 
0.1%

model_hashed_5
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37034 
1.0
 
449
-1.0
 
123

Length

Max length 4
Median length 3
Mean length 3.0032708
Min length 3

Characters and Unicode

Total characters 112941
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 1.0

Common Values

Value Count Frequency (%)
0.0 37034
98.5%
1.0 449
 
1.2%
-1.0 123
 
0.3%

Length

2024-05-20T00:01:58.833493 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:58.963126 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37034
98.5%
1.0 572
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112941
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112941
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112941
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74640
66.1%
. 37606
33.3%
1 572
 
0.5%
- 123
 
0.1%

model_hashed_6
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37262 
1.0
 
200
-1.0
 
144

Length

Max length 4
Median length 3
Mean length 3.0038292
Min length 3

Characters and Unicode

Total characters 112962
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37262
99.1%
1.0 200
 
0.5%
-1.0 144
 
0.4%

Length

2024-05-20T00:01:59.134276 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:59.259931 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37262
99.1%
1.0 344
 
0.9%

Most occurring characters

Value Count Frequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112962
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112962
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112962
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74868
66.3%
. 37606
33.3%
1 344
 
0.3%
- 144
 
0.1%

model_hashed_7
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36451 
-1.0
 
665
1.0
 
490

Length

Max length 4
Median length 3
Mean length 3.0176833
Min length 3

Characters and Unicode

Total characters 113483
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36451
96.9%
-1.0 665
 
1.8%
1.0 490
 
1.3%

Length

2024-05-20T00:01:59.392040 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:59.538620 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36451
96.9%
1.0 1155
 
3.1%

Most occurring characters

Value Count Frequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 113483
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113483
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113483
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74057
65.3%
. 37606
33.1%
1 1155
 
1.0%
- 665
 
0.6%

model_hashed_8
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37053 
1.0
 
523
-1.0
 
30

Length

Max length 4
Median length 3
Mean length 3.0007977
Min length 3

Characters and Unicode

Total characters 112848
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37053
98.5%
1.0 523
 
1.4%
-1.0 30
 
0.1%

Length

2024-05-20T00:01:59.697610 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:01:59.814145 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37053
98.5%
1.0 553
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112848
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112848
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112848
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74659
66.2%
. 37606
33.3%
1 553
 
0.5%
- 30
 
< 0.1%

model_hashed_9
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36719 
1.0
 
505
-1.0
 
382

Length

Max length 4
Median length 3
Mean length 3.010158
Min length 3

Characters and Unicode

Total characters 113200
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36719
97.6%
1.0 505
 
1.3%
-1.0 382
 
1.0%

Length

2024-05-20T00:01:59.931710 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.054977 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36719
97.6%
1.0 887
 
2.4%

Most occurring characters

Value Count Frequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113200
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113200
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113200
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74325
65.7%
. 37606
33.2%
1 887
 
0.8%
- 382
 
0.3%

model_hashed_10
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37056 
1.0
 
281
-1.0
 
269

Length

Max length 4
Median length 3
Mean length 3.0071531
Min length 3

Characters and Unicode

Total characters 113087
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37056
98.5%
1.0 281
 
0.7%
-1.0 269
 
0.7%

Length

2024-05-20T00:02:00.181098 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.299642 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37056
98.5%
1.0 550
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 113087
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113087
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113087
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74662
66.0%
. 37606
33.3%
1 550
 
0.5%
- 269
 
0.2%

model_hashed_11
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37321 
1.0
 
186
-1.0
 
99

Length

Max length 4
Median length 3
Mean length 3.0026326
Min length 3

Characters and Unicode

Total characters 112917
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37321
99.2%
1.0 186
 
0.5%
-1.0 99
 
0.3%

Length

2024-05-20T00:02:00.424178 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.541732 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37321
99.2%
1.0 285
 
0.8%

Most occurring characters

Value Count Frequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112917
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112917
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112917
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74927
66.4%
. 37606
33.3%
1 285
 
0.3%
- 99
 
0.1%

model_hashed_12
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36112 
-1.0
 
1155
1.0
 
339

Length

Max length 4
Median length 3
Mean length 3.0307132
Min length 3

Characters and Unicode

Total characters 113973
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36112
96.0%
-1.0 1155
 
3.1%
1.0 339
 
0.9%

Length

2024-05-20T00:02:00.664365 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:00.793517 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36112
96.0%
1.0 1494
 
4.0%

Most occurring characters

Value Count Frequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 113973
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113973
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113973
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73718
64.7%
. 37606
33.0%
1 1494
 
1.3%
- 1155
 
1.0%

model_hashed_13
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36053 
1.0
 
901
-1.0
 
652

Length

Max length 4
Median length 3
Mean length 3.0173377
Min length 3

Characters and Unicode

Total characters 113470
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36053
95.9%
1.0 901
 
2.4%
-1.0 652
 
1.7%

Length

2024-05-20T00:02:00.934077 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.053706 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36053
95.9%
1.0 1553
 
4.1%

Most occurring characters

Value Count Frequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 113470
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113470
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113470
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73659
64.9%
. 37606
33.1%
1 1553
 
1.4%
- 652
 
0.6%

model_hashed_14
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36578 
1.0
 
614
-1.0
 
414

Length

Max length 4
Median length 3
Mean length 3.0110089
Min length 3

Characters and Unicode

Total characters 113232
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36578
97.3%
1.0 614
 
1.6%
-1.0 414
 
1.1%

Length

2024-05-20T00:02:01.192671 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.308068 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36578
97.3%
1.0 1028
 
2.7%

Most occurring characters

Value Count Frequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113232
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113232
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113232
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74184
65.5%
. 37606
33.2%
1 1028
 
0.9%
- 414
 
0.4%

model_hashed_15
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37332 
-1.0
 
176
1.0
 
98

Length

Max length 4
Median length 3
Mean length 3.0046801
Min length 3

Characters and Unicode

Total characters 112994
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37332
99.3%
-1.0 176
 
0.5%
1.0 98
 
0.3%

Length

2024-05-20T00:02:01.429625 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.551946 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37332
99.3%
1.0 274
 
0.7%

Most occurring characters

Value Count Frequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112994
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112994
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112994
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74938
66.3%
. 37606
33.3%
1 274
 
0.2%
- 176
 
0.2%

model_hashed_16
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36921 
-1.0
 
625
1.0
 
60

Length

Max length 4
Median length 3
Mean length 3.0166197
Min length 3

Characters and Unicode

Total characters 113443
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36921
98.2%
-1.0 625
 
1.7%
1.0 60
 
0.2%

Length

2024-05-20T00:02:01.711141 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:01.894749 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36921
98.2%
1.0 685
 
1.8%

Most occurring characters

Value Count Frequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 113443
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113443
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113443
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74527
65.7%
. 37606
33.1%
1 685
 
0.6%
- 625
 
0.6%

model_hashed_17
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36827 
-1.0
 
447
1.0
 
332

Length

Max length 4
Median length 3
Mean length 3.0118864
Min length 3

Characters and Unicode

Total characters 113265
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36827
97.9%
-1.0 447
 
1.2%
1.0 332
 
0.9%

Length

2024-05-20T00:02:02.026742 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:02.137288 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36827
97.9%
1.0 779
 
2.1%

Most occurring characters

Value Count Frequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113265
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113265
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113265
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74433
65.7%
. 37606
33.2%
1 779
 
0.7%
- 447
 
0.4%

model_hashed_18
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36875 
1.0
 
664
-1.0
 
67

Length

Max length 4
Median length 3
Mean length 3.0017816
Min length 3

Characters and Unicode

Total characters 112885
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36875
98.1%
1.0 664
 
1.8%
-1.0 67
 
0.2%

Length

2024-05-20T00:02:02.261944 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:02.375977 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36875
98.1%
1.0 731
 
1.9%

Most occurring characters

Value Count Frequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112885
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112885
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112885
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74481
66.0%
. 37606
33.3%
1 731
 
0.6%
- 67
 
0.1%

model_hashed_19
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36693 
1.0
 
667
-1.0
 
246

Length

Max length 4
Median length 3
Mean length 3.0065415
Min length 3

Characters and Unicode

Total characters 113064
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36693
97.6%
1.0 667
 
1.8%
-1.0 246
 
0.7%

Length

2024-05-20T00:02:02.499500 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:02.617044 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36693
97.6%
1.0 913
 
2.4%

Most occurring characters

Value Count Frequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 113064
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113064
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113064
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74299
65.7%
. 37606
33.3%
1 913
 
0.8%
- 246
 
0.2%

model_hashed_20
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36118 
1.0
 
925
-1.0
 
563

Length

Max length 4
Median length 3
Mean length 3.014971
Min length 3

Characters and Unicode

Total characters 113381
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36118
96.0%
1.0 925
 
2.5%
-1.0 563
 
1.5%

Length

2024-05-20T00:02:02.797797 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.048997 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36118
96.0%
1.0 1488
 
4.0%

Most occurring characters

Value Count Frequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 113381
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113381
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113381
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73724
65.0%
. 37606
33.2%
1 1488
 
1.3%
- 563
 
0.5%

model_hashed_21
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37118 
1.0
 
402
-1.0
 
86

Length

Max length 4
Median length 3
Mean length 3.0022869
Min length 3

Characters and Unicode

Total characters 112904
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37118
98.7%
1.0 402
 
1.1%
-1.0 86
 
0.2%

Length

2024-05-20T00:02:03.209692 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.408222 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37118
98.7%
1.0 488
 
1.3%

Most occurring characters

Value Count Frequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112904
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112904
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112904
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74724
66.2%
. 37606
33.3%
1 488
 
0.4%
- 86
 
0.1%

model_hashed_22
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36642 
-1.0
 
770
1.0
 
194

Length

Max length 4
Median length 3
Mean length 3.0204755
Min length 3

Characters and Unicode

Total characters 113588
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36642
97.4%
-1.0 770
 
2.0%
1.0 194
 
0.5%

Length

2024-05-20T00:02:03.537833 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.671451 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36642
97.4%
1.0 964
 
2.6%

Most occurring characters

Value Count Frequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 113588
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113588
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113588
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74248
65.4%
. 37606
33.1%
1 964
 
0.8%
- 770
 
0.7%

model_hashed_23
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36672 
1.0
 
510
-1.0
 
424

Length

Max length 4
Median length 3
Mean length 3.0112748
Min length 3

Characters and Unicode

Total characters 113242
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row -1.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36672
97.5%
1.0 510
 
1.4%
-1.0 424
 
1.1%

Length

2024-05-20T00:02:03.793522 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:03.912077 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36672
97.5%
1.0 934
 
2.5%

Most occurring characters

Value Count Frequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113242
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113242
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113242
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74278
65.6%
. 37606
33.2%
1 934
 
0.8%
- 424
 
0.4%

model_hashed_24
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37034 
1.0
 
354
-1.0
 
218

Length

Max length 4
Median length 3
Mean length 3.0057969
Min length 3

Characters and Unicode

Total characters 113036
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37034
98.5%
1.0 354
 
0.9%
-1.0 218
 
0.6%

Length

2024-05-20T00:02:04.091263 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:04.238870 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37034
98.5%
1.0 572
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 113036
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113036
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113036
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74640
66.0%
. 37606
33.3%
1 572
 
0.5%
- 218
 
0.2%

model_hashed_25
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37085 
-1.0
 
378
1.0
 
143

Length

Max length 4
Median length 3
Mean length 3.0100516
Min length 3

Characters and Unicode

Total characters 113196
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37085
98.6%
-1.0 378
 
1.0%
1.0 143
 
0.4%

Length

2024-05-20T00:02:04.364385 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:04.537313 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37085
98.6%
1.0 521
 
1.4%

Most occurring characters

Value Count Frequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113196
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113196
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113196
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74691
66.0%
. 37606
33.2%
1 521
 
0.5%
- 378
 
0.3%

model_hashed_26
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37214 
1.0
 
352
-1.0
 
40

Length

Max length 4
Median length 3
Mean length 3.0010637
Min length 3

Characters and Unicode

Total characters 112858
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37214
99.0%
1.0 352
 
0.9%
-1.0 40
 
0.1%

Length

2024-05-20T00:02:04.723723 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:04.838237 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37214
99.0%
1.0 392
 
1.0%

Most occurring characters

Value Count Frequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112858
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112858
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112858
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74820
66.3%
. 37606
33.3%
1 392
 
0.3%
- 40
 
< 0.1%

model_hashed_27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36072 
1.0
 
1151
-1.0
 
383

Length

Max length 4
Median length 3
Mean length 3.0101845
Min length 3

Characters and Unicode

Total characters 113201
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 1.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36072
95.9%
1.0 1151
 
3.1%
-1.0 383
 
1.0%

Length

2024-05-20T00:02:04.964685 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:05.080419 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36072
95.9%
1.0 1534
 
4.1%

Most occurring characters

Value Count Frequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113201
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113201
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113201
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73678
65.1%
. 37606
33.2%
1 1534
 
1.4%
- 383
 
0.3%

model_hashed_28
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36730 
-1.0
 
513
1.0
 
363

Length

Max length 4
Median length 3
Mean length 3.0136414
Min length 3

Characters and Unicode

Total characters 113331
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36730
97.7%
-1.0 513
 
1.4%
1.0 363
 
1.0%

Length

2024-05-20T00:02:05.210976 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:05.328117 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36730
97.7%
1.0 876
 
2.3%

Most occurring characters

Value Count Frequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 113331
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113331
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113331
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74336
65.6%
. 37606
33.2%
1 876
 
0.8%
- 513
 
0.5%

model_hashed_29
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36162 
1.0
 
1179
-1.0
 
265

Length

Max length 4
Median length 3
Mean length 3.0070467
Min length 3

Characters and Unicode

Total characters 113083
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36162
96.2%
1.0 1179
 
3.1%
-1.0 265
 
0.7%

Length

2024-05-20T00:02:05.459712 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:05.578277 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36162
96.2%
1.0 1444
 
3.8%

Most occurring characters

Value Count Frequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 113083
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113083
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113083
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73768
65.2%
. 37606
33.3%
1 1444
 
1.3%
- 265
 
0.2%

model_hashed_30
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36654 
1.0
 
495
-1.0
 
457

Length

Max length 4
Median length 3
Mean length 3.0121523
Min length 3

Characters and Unicode

Total characters 113275
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36654
97.5%
1.0 495
 
1.3%
-1.0 457
 
1.2%

Length

2024-05-20T00:02:05.725383 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.013375 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36654
97.5%
1.0 952
 
2.5%

Most occurring characters

Value Count Frequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113275
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113275
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113275
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74260
65.6%
. 37606
33.2%
1 952
 
0.8%
- 457
 
0.4%

model_hashed_31
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37264 
1.0
 
257
-1.0
 
85

Length

Max length 4
Median length 3
Mean length 3.0022603
Min length 3

Characters and Unicode

Total characters 112903
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37264
99.1%
1.0 257
 
0.7%
-1.0 85
 
0.2%

Length

2024-05-20T00:02:06.253615 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.415572 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37264
99.1%
1.0 342
 
0.9%

Most occurring characters

Value Count Frequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112903
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112903
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112903
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74870
66.3%
. 37606
33.3%
1 342
 
0.3%
- 85
 
0.1%

model_hashed_32
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36851 
1.0
 
611
-1.0
 
144

Length

Max length 4
Median length 3
Mean length 3.0038292
Min length 3

Characters and Unicode

Total characters 112962
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36851
98.0%
1.0 611
 
1.6%
-1.0 144
 
0.4%

Length

2024-05-20T00:02:06.620244 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.732797 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36851
98.0%
1.0 755
 
2.0%

Most occurring characters

Value Count Frequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112962
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112962
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112962
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74457
65.9%
. 37606
33.3%
1 755
 
0.7%
- 144
 
0.1%

model_hashed_33
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36829 
-1.0
 
754
1.0
 
23

Length

Max length 4
Median length 3
Mean length 3.02005
Min length 3

Characters and Unicode

Total characters 113572
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row -1.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36829
97.9%
-1.0 754
 
2.0%
1.0 23
 
0.1%

Length

2024-05-20T00:02:06.862355 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:06.979460 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36829
97.9%
1.0 777
 
2.1%

Most occurring characters

Value Count Frequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 113572
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113572
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113572
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74435
65.5%
. 37606
33.1%
1 777
 
0.7%
- 754
 
0.7%

model_hashed_34
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36996 
-1.0
 
331
1.0
 
279

Length

Max length 4
Median length 3
Mean length 3.0088018
Min length 3

Characters and Unicode

Total characters 113149
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36996
98.4%
-1.0 331
 
0.9%
1.0 279
 
0.7%

Length

2024-05-20T00:02:07.106056 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:07.221593 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36996
98.4%
1.0 610
 
1.6%

Most occurring characters

Value Count Frequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113149
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113149
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113149
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74602
65.9%
. 37606
33.2%
1 610
 
0.5%
- 331
 
0.3%

model_hashed_35
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37290 
-1.0
 
167
1.0
 
149

Length

Max length 4
Median length 3
Mean length 3.0044408
Min length 3

Characters and Unicode

Total characters 112985
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37290
99.2%
-1.0 167
 
0.4%
1.0 149
 
0.4%

Length

2024-05-20T00:02:07.347012 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:07.465543 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37290
99.2%
1.0 316
 
0.8%

Most occurring characters

Value Count Frequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112985
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112985
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112985
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74896
66.3%
. 37606
33.3%
1 316
 
0.3%
- 167
 
0.1%

model_hashed_36
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37428 
1.0
 
133
-1.0
 
45

Length

Max length 4
Median length 3
Mean length 3.0011966
Min length 3

Characters and Unicode

Total characters 112863
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37428
99.5%
1.0 133
 
0.4%
-1.0 45
 
0.1%

Length

2024-05-20T00:02:07.586607 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:07.837729 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37428
99.5%
1.0 178
 
0.5%

Most occurring characters

Value Count Frequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112863
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112863
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112863
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75034
66.5%
. 37606
33.3%
1 178
 
0.2%
- 45
 
< 0.1%

model_hashed_37
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36559 
1.0
 
643
-1.0
 
404

Length

Max length 4
Median length 3
Mean length 3.010743
Min length 3

Characters and Unicode

Total characters 113222
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36559
97.2%
1.0 643
 
1.7%
-1.0 404
 
1.1%

Length

2024-05-20T00:02:07.968296 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.080532 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36559
97.2%
1.0 1047
 
2.8%

Most occurring characters

Value Count Frequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113222
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113222
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113222
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74165
65.5%
. 37606
33.2%
1 1047
 
0.9%
- 404
 
0.4%

model_hashed_38
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37064 
-1.0
 
338
1.0
 
204

Length

Max length 4
Median length 3
Mean length 3.0089879
Min length 3

Characters and Unicode

Total characters 113156
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37064
98.6%
-1.0 338
 
0.9%
1.0 204
 
0.5%

Length

2024-05-20T00:02:08.207108 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.316648 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37064
98.6%
1.0 542
 
1.4%

Most occurring characters

Value Count Frequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113156
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113156
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113156
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74670
66.0%
. 37606
33.2%
1 542
 
0.5%
- 338
 
0.3%

model_hashed_39
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37040 
1.0
 
403
-1.0
 
163

Length

Max length 4
Median length 3
Mean length 3.0043344
Min length 3

Characters and Unicode

Total characters 112981
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37040
98.5%
1.0 403
 
1.1%
-1.0 163
 
0.4%

Length

2024-05-20T00:02:08.473624 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.615960 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37040
98.5%
1.0 566
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112981
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112981
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112981
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74646
66.1%
. 37606
33.3%
1 566
 
0.5%
- 163
 
0.1%

model_hashed_40
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36623 
-1.0
 
715
1.0
 
268

Length

Max length 4
Median length 3
Mean length 3.0190129
Min length 3

Characters and Unicode

Total characters 113533
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36623
97.4%
-1.0 715
 
1.9%
1.0 268
 
0.7%

Length

2024-05-20T00:02:08.735563 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:08.870134 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36623
97.4%
1.0 983
 
2.6%

Most occurring characters

Value Count Frequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 113533
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113533
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113533
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74229
65.4%
. 37606
33.1%
1 983
 
0.9%
- 715
 
0.6%

model_hashed_41
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37403 
1.0
 
203

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37403
99.5%
1.0 203
 
0.5%

Length

2024-05-20T00:02:08.995178 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.106785 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37403
99.5%
1.0 203
 
0.5%

Most occurring characters

Value Count Frequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75009
66.5%
. 37606
33.3%
1 203
 
0.2%

model_hashed_42
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37298 
1.0
 
167
-1.0
 
141

Length

Max length 4
Median length 3
Mean length 3.0037494
Min length 3

Characters and Unicode

Total characters 112959
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37298
99.2%
1.0 167
 
0.4%
-1.0 141
 
0.4%

Length

2024-05-20T00:02:09.229301 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.348897 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37298
99.2%
1.0 308
 
0.8%

Most occurring characters

Value Count Frequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112959
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112959
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112959
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74904
66.3%
. 37606
33.3%
1 308
 
0.3%
- 141
 
0.1%

model_hashed_43
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37089 
-1.0
 
486
1.0
 
31

Length

Max length 4
Median length 3
Mean length 3.0129235
Min length 3

Characters and Unicode

Total characters 113304
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37089
98.6%
-1.0 486
 
1.3%
1.0 31
 
0.1%

Length

2024-05-20T00:02:09.473365 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.594412 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37089
98.6%
1.0 517
 
1.4%

Most occurring characters

Value Count Frequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113304
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113304
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113304
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74695
65.9%
. 37606
33.2%
1 517
 
0.5%
- 486
 
0.4%

model_hashed_44
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37432 
1.0
 
124
-1.0
 
50

Length

Max length 4
Median length 3
Mean length 3.0013296
Min length 3

Characters and Unicode

Total characters 112868
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37432
99.5%
1.0 124
 
0.3%
-1.0 50
 
0.1%

Length

2024-05-20T00:02:09.718956 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:09.831515 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37432
99.5%
1.0 174
 
0.5%

Most occurring characters

Value Count Frequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112868
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112868
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112868
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75038
66.5%
. 37606
33.3%
1 174
 
0.2%
- 50
 
< 0.1%

model_hashed_45
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37053 
-1.0
 
342
1.0
 
211

Length

Max length 4
Median length 3
Mean length 3.0090943
Min length 3

Characters and Unicode

Total characters 113160
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37053
98.5%
-1.0 342
 
0.9%
1.0 211
 
0.6%

Length

2024-05-20T00:02:09.964053 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.078845 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37053
98.5%
1.0 553
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113160
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113160
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113160
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74659
66.0%
. 37606
33.2%
1 553
 
0.5%
- 342
 
0.3%

model_hashed_46
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37042 
-1.0
 
301
1.0
 
263

Length

Max length 4
Median length 3
Mean length 3.008004
Min length 3

Characters and Unicode

Total characters 113119
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37042
98.5%
-1.0 301
 
0.8%
1.0 263
 
0.7%

Length

2024-05-20T00:02:10.202919 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.327521 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37042
98.5%
1.0 564
 
1.5%

Most occurring characters

Value Count Frequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113119
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113119
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113119
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74648
66.0%
. 37606
33.2%
1 564
 
0.5%
- 301
 
0.3%

model_hashed_47
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37269 
-1.0
 
293
1.0
 
44

Length

Max length 4
Median length 3
Mean length 3.0077913
Min length 3

Characters and Unicode

Total characters 113111
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37269
99.1%
-1.0 293
 
0.8%
1.0 44
 
0.1%

Length

2024-05-20T00:02:10.468178 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.584783 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37269
99.1%
1.0 337
 
0.9%

Most occurring characters

Value Count Frequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113111
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113111
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113111
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74875
66.2%
. 37606
33.2%
1 337
 
0.3%
- 293
 
0.3%

model_hashed_48
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35731 
-1.0
 
1212
1.0
 
663

Length

Max length 4
Median length 3
Mean length 3.0322289
Min length 3

Characters and Unicode

Total characters 114030
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row -1.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 35731
95.0%
-1.0 1212
 
3.2%
1.0 663
 
1.8%

Length

2024-05-20T00:02:10.796407 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:10.916600 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35731
95.0%
1.0 1875
 
5.0%

Most occurring characters

Value Count Frequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 114030
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 114030
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 114030
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73337
64.3%
. 37606
33.0%
1 1875
 
1.6%
- 1212
 
1.1%

model_hashed_49
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37255 
-1.0
 
240
1.0
 
111

Length

Max length 4
Median length 3
Mean length 3.006382
Min length 3

Characters and Unicode

Total characters 113058
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37255
99.1%
-1.0 240
 
0.6%
1.0 111
 
0.3%

Length

2024-05-20T00:02:11.082760 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:11.279464 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37255
99.1%
1.0 351
 
0.9%

Most occurring characters

Value Count Frequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 113058
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113058
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113058
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74861
66.2%
. 37606
33.3%
1 351
 
0.3%
- 240
 
0.2%

model_hashed_50
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37063 
-1.0
 
395
1.0
 
148

Length

Max length 4
Median length 3
Mean length 3.0105036
Min length 3

Characters and Unicode

Total characters 113213
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37063
98.6%
-1.0 395
 
1.1%
1.0 148
 
0.4%

Length

2024-05-20T00:02:11.588871 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:11.902299 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37063
98.6%
1.0 543
 
1.4%

Most occurring characters

Value Count Frequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 113213
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113213
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113213
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74669
66.0%
. 37606
33.2%
1 543
 
0.5%
- 395
 
0.3%

model_hashed_51
Categorical

IMBALANCE 

Distinct 3
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36614 
-1.0
 
501
1.0
 
491

Length

Max length 4
Median length 3
Mean length 3.0133223
Min length 3

Characters and Unicode

Total characters 113319
Distinct characters 4
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36614
97.4%
-1.0 501
 
1.3%
1.0 491
 
1.3%

Length

2024-05-20T00:02:12.174499 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:12.309930 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36614
97.4%
1.0 992
 
2.6%

Most occurring characters

Value Count Frequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 113319
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 113319
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 113319
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74220
65.5%
. 37606
33.2%
1 992
 
0.9%
- 501
 
0.4%

exterior_color_x0
Real number (ℝ)

HIGH CORRELATION 

Distinct 1898
Distinct (%) 5.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -0.24655131
Minimum -3.0589778
Maximum 1.7655432
Zeros 30
Zeros (%) 0.1%
Negative 25277
Negative (%) 67.2%
Memory size 293.9 KiB
2024-05-20T00:02:12.629329 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -3.0589778
5-th percentile -1.6798811
Q1 -0.84383506
median -0.43715855
Q3 0.2650055
95-th percentile 1.2626994
Maximum 1.7655432
Range 4.8245211
Interquartile range (IQR) 1.1088406

Descriptive statistics

Standard deviation 0.9208845
Coefficient of variation (CV) -3.7350624
Kurtosis 0.04737661
Mean -0.24655131
Median Absolute Deviation (MAD) 0.48601467
Skewness 0.12766741
Sum -9271.8084
Variance 0.84802827
Monotonicity Not monotonic
2024-05-20T00:02:12.794462 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.7511814237 2282
 
6.1%
1.60383141 952
 
2.5%
1.256851673 929
 
2.5%
-0.5318110585 911
 
2.4%
0.451739043 681
 
1.8%
-0.03456513211 488
 
1.3%
-0.9413885474 434
 
1.2%
-0.993098259 429
 
1.1%
-0.2056239396 428
 
1.1%
-3.058977842 409
 
1.1%
Other values (1888) 29663
78.9%
Value Count Frequency (%)
-3.058977842 409
1.1%
-2.341434002 153
 
0.4%
-2.117038488 1
 
< 0.1%
-1.961747289 2
 
< 0.1%
-1.952655673 1
 
< 0.1%
-1.905079603 10
 
< 0.1%
-1.878854156 5
 
< 0.1%
-1.868785262 1
 
< 0.1%
-1.859131932 1
 
< 0.1%
-1.840353489 1
 
< 0.1%
Value Count Frequency (%)
1.765543222 48
 
0.1%
1.761154652 8
 
< 0.1%
1.717839837 78
 
0.2%
1.701323867 1
 
< 0.1%
1.606879234 137
 
0.4%
1.60383141 952
2.5%
1.572880983 5
 
< 0.1%
1.545447469 4
 
< 0.1%
1.539661765 2
 
< 0.1%
1.537572265 16
 
< 0.1%

exterior_color_x1
Real number (ℝ)

Distinct 1898
Distinct (%) 5.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.70135798
Minimum -1.0462689
Maximum 2.3650715
Zeros 30
Zeros (%) 0.1%
Negative 3698
Negative (%) 9.8%
Memory size 293.9 KiB
2024-05-20T00:02:13.005778 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -1.0462689
5-th percentile -0.081496693
Q1 0.17771569
median 0.64382613
Q3 1.1491094
95-th percentile 1.7051448
Maximum 2.3650715
Range 3.4113405
Interquartile range (IQR) 0.97139367

Descriptive statistics

Standard deviation 0.59193777
Coefficient of variation (CV) 0.84398808
Kurtosis -0.75849394
Mean 0.70135798
Median Absolute Deviation (MAD) 0.4695313
Skewness 0.40349357
Sum 26375.268
Variance 0.35039032
Monotonicity Not monotonic
2024-05-20T00:02:13.171595 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1.597052574 2282
 
6.1%
0.6729584932 952
 
2.5%
0.7106455564 929
 
2.5%
-0.02926694602 911
 
2.4%
1.337975144 681
 
1.8%
0.6428204775 488
 
1.3%
0.05307491124 434
 
1.2%
0.8483321071 429
 
1.1%
1.591193676 428
 
1.1%
0.1777156889 409
 
1.1%
Other values (1888) 29663
78.9%
Value Count Frequency (%)
-1.04626894 40
0.1%
-0.8405029774 11
 
< 0.1%
-0.6971178055 3
 
< 0.1%
-0.6439523101 1
 
< 0.1%
-0.4955306649 2
 
< 0.1%
-0.4575667381 4
 
< 0.1%
-0.4569045603 4
 
< 0.1%
-0.4348849654 56
0.1%
-0.4342766404 3
 
< 0.1%
-0.4260134697 1
 
< 0.1%
Value Count Frequency (%)
2.365071535 3
 
< 0.1%
2.342634439 1
 
< 0.1%
2.211446524 5
 
< 0.1%
2.118834496 351
0.9%
2.082643747 247
0.7%
1.969843507 1
 
< 0.1%
1.922412515 10
 
< 0.1%
1.913381219 63
 
0.2%
1.90028429 1
 
< 0.1%
1.898656607 105
 
0.3%

exterior_color_x2
Real number (ℝ)

HIGH CORRELATION 

Distinct 1898
Distinct (%) 5.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.68450674
Minimum -1.2273194
Maximum 2.3864536
Zeros 30
Zeros (%) 0.1%
Negative 4656
Negative (%) 12.4%
Memory size 293.9 KiB
2024-05-20T00:02:13.328686 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -1.2273194
5-th percentile -0.52878088
Q1 0.26537639
median 0.82993811
Q3 1.0931465
95-th percentile 1.3620613
Maximum 2.3864536
Range 3.613773
Interquartile range (IQR) 0.82777014

Descriptive statistics

Standard deviation 0.58743672
Coefficient of variation (CV) 0.85818983
Kurtosis 0.14484869
Mean 0.68450674
Median Absolute Deviation (MAD) 0.3678022
Skewness -0.70184833
Sum 25741.561
Variance 0.3450819
Monotonicity Not monotonic
2024-05-20T00:02:13.501899 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1.30016315 2282
 
6.1%
-0.7183076143 952
 
2.5%
0.2375717759 929
 
2.5%
1.07299602 911
 
2.4%
-0.2575258613 681
 
1.8%
1.05477047 488
 
1.3%
0.7411549687 434
 
1.2%
1.094812393 429
 
1.1%
0.629778266 428
 
1.1%
1.8322438 409
 
1.1%
Other values (1888) 29663
78.9%
Value Count Frequency (%)
-1.22731936 5
 
< 0.1%
-1.160905719 3
 
< 0.1%
-1.153503895 2
 
< 0.1%
-1.063666463 1
 
< 0.1%
-0.9664271474 247
0.7%
-0.9565482736 3
 
< 0.1%
-0.893484056 125
0.3%
-0.890312314 2
 
< 0.1%
-0.8902192116 11
 
< 0.1%
-0.8835706115 39
 
0.1%
Value Count Frequency (%)
2.386453629 4
 
< 0.1%
2.168669939 11
 
< 0.1%
2.016318083 147
 
0.4%
1.919445395 120
 
0.3%
1.8322438 409
1.1%
1.807303309 1
 
< 0.1%
1.776978254 23
 
0.1%
1.758986831 1
 
< 0.1%
1.757221937 2
 
< 0.1%
1.734416485 2
 
< 0.1%

exterior_color_x3
Real number (ℝ)

Distinct 1901
Distinct (%) 5.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -0.40705956
Minimum -3.5100327
Maximum 0.66745049
Zeros 30
Zeros (%) 0.1%
Negative 30886
Negative (%) 82.1%
Memory size 293.9 KiB
2024-05-20T00:02:13.661456 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -3.5100327
5-th percentile -1.3930054
Q1 -0.6146127
median -0.34153304
Q3 -0.063247167
95-th percentile 0.16928968
Maximum 0.66745049
Range 4.1774831
Interquartile range (IQR) 0.55136553

Descriptive statistics

Standard deviation 0.5039437
Coefficient of variation (CV) -1.2380097
Kurtosis 1.2662654
Mean -0.40705956
Median Absolute Deviation (MAD) 0.27828587
Skewness -0.87365066
Sum -15307.882
Variance 0.25395925
Monotonicity Not monotonic
2024-05-20T00:02:13.811517 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.06324716657 2282
 
6.1%
0.1183137298 952
 
2.5%
0.1416909993 929
 
2.5%
-0.2078505009 911
 
2.4%
0.6487338543 681
 
1.8%
-1.367208958 488
 
1.3%
-0.5460887551 434
 
1.2%
-0.4349435866 429
 
1.1%
-0.7438818216 428
 
1.1%
-0.5880366564 409
 
1.1%
Other values (1891) 29663
78.9%
Value Count Frequency (%)
-3.510032654 3
 
< 0.1%
-3.104436636 11
 
< 0.1%
-2.963784218 1
 
< 0.1%
-2.71809268 1
 
< 0.1%
-2.235822678 15
 
< 0.1%
-2.170943499 2
 
< 0.1%
-2.165496588 10
 
< 0.1%
-2.159054995 201
0.5%
-2.116509914 1
 
< 0.1%
-2.051227331 3
 
< 0.1%
Value Count Frequency (%)
0.6674504876 2
 
< 0.1%
0.6487338543 681
1.8%
0.6393755674 24
 
0.1%
0.5860561132 5
 
< 0.1%
0.5817661285 1
 
< 0.1%
0.5564879775 1
 
< 0.1%
0.5485950708 210
 
0.6%
0.5377053022 20
 
0.1%
0.5104919076 113
 
0.3%
0.4996722639 21
 
0.1%

exterior_color_x4
Real number (ℝ)

HIGH CORRELATION 

Distinct 1897
Distinct (%) 5.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -1.0504987
Minimum -3.2427816
Maximum 0.19847639
Zeros 30
Zeros (%) 0.1%
Negative 37375
Negative (%) 99.4%
Memory size 293.9 KiB
2024-05-20T00:02:13.985967 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -3.2427816
5-th percentile -2.2842441
Q1 -1.4921011
median -0.88851881
Q3 -0.47605655
95-th percentile -0.26058449
Maximum 0.19847639
Range 3.441258
Interquartile range (IQR) 1.0160445

Descriptive statistics

Standard deviation 0.65526324
Coefficient of variation (CV) -0.62376399
Kurtosis 0.32278
Mean -1.0504987
Median Absolute Deviation (MAD) 0.44773757
Skewness -0.87762716
Sum -39505.053
Variance 0.42936991
Monotonicity Not monotonic
2024-05-20T00:02:14.158768 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.4289364517 2282
 
6.1%
-2.859434128 952
 
2.5%
-1.756619096 929
 
2.5%
-0.9457932711 911
 
2.4%
-2.24461627 681
 
1.8%
-0.4710775912 488
 
1.3%
-0.8093400598 434
 
1.2%
-0.4450545013 429
 
1.1%
-0.4509477317 428
 
1.1%
-1.575397968 409
 
1.1%
Other values (1887) 29663
78.9%
Value Count Frequency (%)
-3.242781639 48
 
0.1%
-2.988175392 170
 
0.5%
-2.926416874 1
 
< 0.1%
-2.893342972 8
 
< 0.1%
-2.888619423 5
 
< 0.1%
-2.859434128 952
2.5%
-2.797168732 2
 
< 0.1%
-2.766561985 5
 
< 0.1%
-2.752726316 58
 
0.2%
-2.718497753 1
 
< 0.1%
Value Count Frequency (%)
0.1984763891 2
 
< 0.1%
0.1844702512 7
 
< 0.1%
0.1816361398 1
 
< 0.1%
0.1806037724 1
 
< 0.1%
0.1727492064 1
 
< 0.1%
0.1634026617 1
 
< 0.1%
0.1547729522 22
0.1%
0.1407849342 1
 
< 0.1%
0.1399295032 1
 
< 0.1%
0.1376764029 1
 
< 0.1%

interior_color_x0
Real number (ℝ)

HIGH CORRELATION 

Distinct 1005
Distinct (%) 2.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -0.47377412
Minimum -2.3731728
Maximum 0.19349997
Zeros 124
Zeros (%) 0.3%
Negative 35383
Negative (%) 94.1%
Memory size 293.9 KiB
2024-05-20T00:02:14.347728 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -2.3731728
5-th percentile -0.87139606
Q1 -0.51743746
median -0.49426496
Q3 -0.35561335
95-th percentile 0.14623532
Maximum 0.19349997
Range 2.5666727
Interquartile range (IQR) 0.16182411

Descriptive statistics

Standard deviation 0.25116765
Coefficient of variation (CV) -0.53014218
Kurtosis 8.170376
Mean -0.47377412
Median Absolute Deviation (MAD) 0.076409936
Skewness -0.80543476
Sum -17816.75
Variance 0.063085186
Monotonicity Not monotonic
2024-05-20T00:02:14.549051 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.4942649603 14959
39.8%
-0.3139528036 3031
 
8.1%
-0.517437458 2378
 
6.3%
-0.7491714954 2154
 
5.7%
0.146235317 1875
 
5.0%
-0.3314826488 1711
 
4.5%
-0.3556133509 909
 
2.4%
-0.4178550243 560
 
1.5%
-0.2350679487 503
 
1.3%
-1.061753273 394
 
1.0%
Other values (995) 9132
24.3%
Value Count Frequency (%)
-2.37317276 74
0.2%
-2.330812693 2
 
< 0.1%
-1.696282983 6
 
< 0.1%
-1.683698058 1
 
< 0.1%
-1.663648725 3
 
< 0.1%
-1.640157223 60
0.2%
-1.605787754 1
 
< 0.1%
-1.527393103 1
 
< 0.1%
-1.451779842 6
 
< 0.1%
-1.370571852 16
 
< 0.1%
Value Count Frequency (%)
0.1934999675 27
 
0.1%
0.1841681004 2
 
< 0.1%
0.1838356555 1
 
< 0.1%
0.1769794077 3
 
< 0.1%
0.174426958 1
 
< 0.1%
0.1719948053 2
 
< 0.1%
0.1622290909 1
 
< 0.1%
0.1616194546 2
 
< 0.1%
0.1474763155 7
 
< 0.1%
0.146235317 1875
5.0%

interior_color_x1
Real number (ℝ)

HIGH CORRELATION 

Distinct 1006
Distinct (%) 2.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.46271089
Minimum -0.18563056
Maximum 1.617914
Zeros 124
Zeros (%) 0.3%
Negative 338
Negative (%) 0.9%
Memory size 293.9 KiB
2024-05-20T00:02:14.704234 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -0.18563056
5-th percentile 0.10140524
Q1 0.38753486
median 0.38753486
Q3 0.54801261
95-th percentile 0.82123405
Maximum 1.617914
Range 1.8035445
Interquartile range (IQR) 0.16047776

Descriptive statistics

Standard deviation 0.22646634
Coefficient of variation (CV) 0.48943377
Kurtosis 1.7542784
Mean 0.46271089
Median Absolute Deviation (MAD) 0.11726177
Skewness 0.89298021
Sum 17400.706
Variance 0.051287001
Monotonicity Not monotonic
2024-05-20T00:02:14.985948 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.3875348568 14959
39.8%
0.5480126143 3031
 
8.1%
0.8212340474 2378
 
6.3%
0.7358144522 2154
 
5.7%
0.1014052406 1875
 
5.0%
0.2289542705 1711
 
4.5%
0.3029362559 909
 
2.4%
0.4055069983 560
 
1.5%
0.2473456115 503
 
1.3%
1.219044447 394
 
1.0%
Other values (996) 9132
24.3%
Value Count Frequency (%)
-0.1856305599 2
 
< 0.1%
-0.1821084023 5
 
< 0.1%
-0.1731399596 1
 
< 0.1%
-0.1720187366 2
 
< 0.1%
-0.1697011441 1
 
< 0.1%
-0.1592723578 1
 
< 0.1%
-0.1557089537 27
0.1%
-0.153109625 1
 
< 0.1%
-0.1529581994 1
 
< 0.1%
-0.145032689 4
 
< 0.1%
Value Count Frequency (%)
1.617913961 7
 
< 0.1%
1.490871191 74
 
0.2%
1.345086932 6
 
< 0.1%
1.267589569 11
 
< 0.1%
1.265928507 2
 
< 0.1%
1.264083266 2
 
< 0.1%
1.241563797 6
 
< 0.1%
1.219044447 394
1.0%
1.215440989 2
 
< 0.1%
1.209338069 1
 
< 0.1%

interior_color_x2
Real number (ℝ)

HIGH CORRELATION 

Distinct 1007
Distinct (%) 2.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.58657566
Minimum -0.19890115
Maximum 1.6741534
Zeros 124
Zeros (%) 0.3%
Negative 317
Negative (%) 0.8%
Memory size 293.9 KiB
2024-05-20T00:02:15.202469 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -0.19890115
5-th percentile 0.13515386
Q1 0.3568562
median 0.5897873
Q3 0.5897873
95-th percentile 1.2861211
Maximum 1.6741534
Range 1.8730546
Interquartile range (IQR) 0.23293111

Descriptive statistics

Standard deviation 0.3028972
Coefficient of variation (CV) 0.51638216
Kurtosis 0.54138951
Mean 0.58657566
Median Absolute Deviation (MAD) 0.16120607
Skewness 0.7160801
Sum 22058.764
Variance 0.091746716
Monotonicity Not monotonic
2024-05-20T00:02:15.377670 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.5897873044 14959
39.8%
0.3238786161 3031
 
8.1%
0.9810428023 2378
 
6.3%
1.28612113 2154
 
5.7%
0.1351538599 1875
 
5.0%
0.3568561971 1711
 
4.5%
0.1501284093 909
 
2.4%
0.2745200992 560
 
1.5%
0.3745622039 503
 
1.3%
0.4491575062 394
 
1.0%
Other values (997) 9132
24.3%
Value Count Frequency (%)
-0.1989011467 27
0.1%
-0.1926358491 1
 
< 0.1%
-0.1918946207 2
 
< 0.1%
-0.1850786656 1
 
< 0.1%
-0.1691945791 2
 
< 0.1%
-0.1667596102 2
 
< 0.1%
-0.157146126 2
 
< 0.1%
-0.1557633877 1
 
< 0.1%
-0.1498440206 2
 
< 0.1%
-0.1477413625 1
 
< 0.1%
Value Count Frequency (%)
1.674153447 7
 
< 0.1%
1.663210392 11
 
< 0.1%
1.644211531 46
0.1%
1.569934249 7
 
< 0.1%
1.5572685 1
 
< 0.1%
1.524847984 4
 
< 0.1%
1.520518303 30
0.1%
1.484605789 2
 
< 0.1%
1.480137348 24
0.1%
1.472362518 1
 
< 0.1%

interior_color_x3
Real number (ℝ)

HIGH CORRELATION 

Distinct 1006
Distinct (%) 2.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -0.018644563
Minimum -1.3000717
Maximum 0.33831894
Zeros 124
Zeros (%) 0.3%
Negative 14624
Negative (%) 38.9%
Memory size 293.9 KiB
2024-05-20T00:02:15.612821 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -1.3000717
5-th percentile -0.67214864
Q1 -0.32673895
median 0.080759585
Q3 0.33831894
95-th percentile 0.33831894
Maximum 0.33831894
Range 1.6383907
Interquartile range (IQR) 0.6650579

Descriptive statistics

Standard deviation 0.36924141
Coefficient of variation (CV) -19.80424
Kurtosis -0.96136982
Mean -0.018644563
Median Absolute Deviation (MAD) 0.25755936
Skewness -0.6166468
Sum -701.14745
Variance 0.13633922
Monotonicity Not monotonic
2024-05-20T00:02:15.876506 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0.338318944 14959
39.8%
0.0807595849 3031
 
8.1%
-0.6057642102 2378
 
6.3%
-0.6721486449 2154
 
5.7%
0.01525731105 1875
 
5.0%
-0.3267389536 1711
 
4.5%
0.1106414646 909
 
2.4%
0.08260063827 560
 
1.5%
-0.2243861854 503
 
1.3%
-0.3761080205 394
 
1.0%
Other values (996) 9132
24.3%
Value Count Frequency (%)
-1.300071716 2
 
< 0.1%
-1.109388828 4
 
< 0.1%
-1.014641404 1
 
< 0.1%
-1.009524941 2
 
< 0.1%
-0.9533045888 4
 
< 0.1%
-0.9446456432 1
 
< 0.1%
-0.9407006502 46
0.1%
-0.9355012774 7
 
< 0.1%
-0.9269840717 11
 
< 0.1%
-0.9198940992 74
0.2%
Value Count Frequency (%)
0.338318944 14959
39.8%
0.2751825452 18
 
< 0.1%
0.2697351873 2
 
< 0.1%
0.2643211782 2
 
< 0.1%
0.2388735563 10
 
< 0.1%
0.2378211021 6
 
< 0.1%
0.2348012477 14
 
< 0.1%
0.2329705656 3
 
< 0.1%
0.2253863811 1
 
< 0.1%
0.2184856087 8
 
< 0.1%

interior_color_x4
Real number (ℝ)

HIGH CORRELATION 

Distinct 1006
Distinct (%) 2.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -0.18183252
Minimum -0.49870196
Maximum 0.81330609
Zeros 124
Zeros (%) 0.3%
Negative 23633
Negative (%) 62.8%
Memory size 293.9 KiB
2024-05-20T00:02:16.034625 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -0.49870196
5-th percentile -0.49870196
Q1 -0.49870196
median -0.19450934
Q3 0.055999383
95-th percentile 0.25908411
Maximum 0.81330609
Range 1.3120081
Interquartile range (IQR) 0.55470134

Descriptive statistics

Standard deviation 0.29525218
Coefficient of variation (CV) -1.623759
Kurtosis -0.91806617
Mean -0.18183252
Median Absolute Deviation (MAD) 0.30419262
Skewness 0.33971558
Sum -6837.9936
Variance 0.087173852
Monotonicity Not monotonic
2024-05-20T00:02:16.279870 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-0.4987019598 14959
39.8%
-0.1945093423 3031
 
8.1%
0.1044528782 2378
 
6.3%
0.05599938333 2154
 
5.7%
0.1270178109 1875
 
5.0%
0.01482179761 1711
 
4.5%
-0.2170253694 909
 
2.4%
-0.08903948963 560
 
1.5%
0.1336151361 503
 
1.3%
-0.05107550323 394
 
1.0%
Other values (996) 9132
24.3%
Value Count Frequency (%)
-0.4987019598 14959
39.8%
-0.3876874447 1
 
< 0.1%
-0.3491453528 1
 
< 0.1%
-0.3234826028 24
 
0.1%
-0.3229342401 1
 
< 0.1%
-0.3228434324 118
 
0.3%
-0.3218908906 1
 
< 0.1%
-0.3077826202 3
 
< 0.1%
-0.3023605645 2
 
< 0.1%
-0.2900065184 2
 
< 0.1%
Value Count Frequency (%)
0.8133060932 1
 
< 0.1%
0.7796087861 60
0.2%
0.7773840427 2
 
< 0.1%
0.7301509976 2
 
< 0.1%
0.7077647448 2
 
< 0.1%
0.7020905018 1
 
< 0.1%
0.6245722771 4
 
< 0.1%
0.6023901701 99
0.3%
0.5960175991 1
 
< 0.1%
0.5875912905 45
0.1%

drivetrain_All-wheel Drive
Categorical

HIGH CORRELATION 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
1.0
28703 
0.0
8903 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 0.0
3rd row 1.0
4th row 1.0
5th row 0.0

Common Values

Value Count Frequency (%)
1.0 28703
76.3%
0.0 8903
 
23.7%

Length

2024-05-20T00:02:16.455031 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:16.595145 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
1.0 28703
76.3%
0.0 8903
 
23.7%

Most occurring characters

Value Count Frequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 46509
41.2%
. 37606
33.3%
1 28703
25.4%

drivetrain_Front-wheel Drive
Categorical

HIGH CORRELATION 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
30286 
1.0
7320 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 1.0
3rd row 0.0
4th row 0.0
5th row 1.0

Common Values

Value Count Frequency (%)
0.0 30286
80.5%
1.0 7320
 
19.5%

Length

2024-05-20T00:02:16.805110 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:16.923971 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 30286
80.5%
1.0 7320
 
19.5%

Most occurring characters

Value Count Frequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 67892
60.2%
. 37606
33.3%
1 7320
 
6.5%

drivetrain_Rear-wheel Drive
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36023 
1.0
 
1583

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36023
95.8%
1.0 1583
 
4.2%

Length

2024-05-20T00:02:17.117190 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:17.242292 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36023
95.8%
1.0 1583
 
4.2%

Most occurring characters

Value Count Frequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73629
65.3%
. 37606
33.3%
1 1583
 
1.4%

make_Acura
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36842 
1.0
 
764

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36842
98.0%
1.0 764
 
2.0%

Length

2024-05-20T00:02:17.453200 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:17.602401 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36842
98.0%
1.0 764
 
2.0%

Most occurring characters

Value Count Frequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74448
66.0%
. 37606
33.3%
1 764
 
0.7%

make_Audi
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36356 
1.0
 
1250

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 1.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36356
96.7%
1.0 1250
 
3.3%

Length

2024-05-20T00:02:17.796268 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:17.914613 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36356
96.7%
1.0 1250
 
3.3%

Most occurring characters

Value Count Frequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73962
65.6%
. 37606
33.3%
1 1250
 
1.1%

make_BMW
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35744 
1.0
 
1862

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 35744
95.0%
1.0 1862
 
5.0%

Length

2024-05-20T00:02:18.073920 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:18.243054 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35744
95.0%
1.0 1862
 
5.0%

Most occurring characters

Value Count Frequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73350
65.0%
. 37606
33.3%
1 1862
 
1.7%

make_Buick
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37142 
1.0
 
464

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37142
98.8%
1.0 464
 
1.2%

Length

2024-05-20T00:02:18.394811 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:18.509109 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37142
98.8%
1.0 464
 
1.2%

Most occurring characters

Value Count Frequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74748
66.3%
. 37606
33.3%
1 464
 
0.4%

make_Cadillac
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36409 
1.0
 
1197

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36409
96.8%
1.0 1197
 
3.2%

Length

2024-05-20T00:02:18.633378 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:18.764982 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36409
96.8%
1.0 1197
 
3.2%

Most occurring characters

Value Count Frequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74015
65.6%
. 37606
33.3%
1 1197
 
1.1%

make_Chevrolet
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
33891 
1.0
3715 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 1.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 33891
90.1%
1.0 3715
 
9.9%

Length

2024-05-20T00:02:18.917772 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:19.108776 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 33891
90.1%
1.0 3715
 
9.9%

Most occurring characters

Value Count Frequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 71497
63.4%
. 37606
33.3%
1 3715
 
3.3%

make_Dodge
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36536 
1.0
 
1070

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 1.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36536
97.2%
1.0 1070
 
2.8%

Length

2024-05-20T00:02:19.346474 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:19.457358 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36536
97.2%
1.0 1070
 
2.8%

Most occurring characters

Value Count Frequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74142
65.7%
. 37606
33.3%
1 1070
 
0.9%

make_Ford
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
34149 
1.0
3457 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 34149
90.8%
1.0 3457
 
9.2%

Length

2024-05-20T00:02:19.812539 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:19.993948 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 34149
90.8%
1.0 3457
 
9.2%

Most occurring characters

Value Count Frequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 71755
63.6%
. 37606
33.3%
1 3457
 
3.1%

make_GMC
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36748 
1.0
 
858

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36748
97.7%
1.0 858
 
2.3%

Length

2024-05-20T00:02:20.137169 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:20.250551 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36748
97.7%
1.0 858
 
2.3%

Most occurring characters

Value Count Frequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74354
65.9%
. 37606
33.3%
1 858
 
0.8%

make_Honda
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36286 
1.0
 
1320

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36286
96.5%
1.0 1320
 
3.5%

Length

2024-05-20T00:02:20.375465 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:20.481752 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36286
96.5%
1.0 1320
 
3.5%

Most occurring characters

Value Count Frequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73892
65.5%
. 37606
33.3%
1 1320
 
1.2%

make_Hyundai
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35178 
1.0
 
2428

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 35178
93.5%
1.0 2428
 
6.5%

Length

2024-05-20T00:02:20.608766 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:20.774891 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35178
93.5%
1.0 2428
 
6.5%

Most occurring characters

Value Count Frequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 72784
64.5%
. 37606
33.3%
1 2428
 
2.2%

make_INFINITI
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37023 
1.0
 
583

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37023
98.4%
1.0 583
 
1.6%

Length

2024-05-20T00:02:20.896241 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.090583 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37023
98.4%
1.0 583
 
1.6%

Most occurring characters

Value Count Frequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74629
66.1%
. 37606
33.3%
1 583
 
0.5%

make_Jeep
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
34624 
1.0
 
2982

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 34624
92.1%
1.0 2982
 
7.9%

Length

2024-05-20T00:02:21.278449 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.398536 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 34624
92.1%
1.0 2982
 
7.9%

Most occurring characters

Value Count Frequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 72230
64.0%
. 37606
33.3%
1 2982
 
2.6%

make_Kia
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36440 
1.0
 
1166

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36440
96.9%
1.0 1166
 
3.1%

Length

2024-05-20T00:02:21.526886 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.636720 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36440
96.9%
1.0 1166
 
3.1%

Most occurring characters

Value Count Frequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74046
65.6%
. 37606
33.3%
1 1166
 
1.0%

make_Land Rover
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37136 
1.0
 
470

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37136
98.8%
1.0 470
 
1.2%

Length

2024-05-20T00:02:21.760659 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:21.989024 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37136
98.8%
1.0 470
 
1.2%

Most occurring characters

Value Count Frequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74742
66.3%
. 37606
33.3%
1 470
 
0.4%

make_Lexus
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36839 
1.0
 
767

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36839
98.0%
1.0 767
 
2.0%

Length

2024-05-20T00:02:22.139499 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.252806 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36839
98.0%
1.0 767
 
2.0%

Most occurring characters

Value Count Frequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74445
66.0%
. 37606
33.3%
1 767
 
0.7%

make_Lincoln
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36888 
1.0
 
718

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36888
98.1%
1.0 718
 
1.9%

Length

2024-05-20T00:02:22.378228 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.488112 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36888
98.1%
1.0 718
 
1.9%

Most occurring characters

Value Count Frequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74494
66.0%
. 37606
33.3%
1 718
 
0.6%

make_Mazda
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36349 
1.0
 
1257

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36349
96.7%
1.0 1257
 
3.3%

Length

2024-05-20T00:02:22.612652 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.731613 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36349
96.7%
1.0 1257
 
3.3%

Most occurring characters

Value Count Frequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73955
65.6%
. 37606
33.3%
1 1257
 
1.1%

make_Mercedes-Benz
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35141 
1.0
 
2465

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 35141
93.4%
1.0 2465
 
6.6%

Length

2024-05-20T00:02:22.876432 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:22.985622 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35141
93.4%
1.0 2465
 
6.6%

Most occurring characters

Value Count Frequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 72747
64.5%
. 37606
33.3%
1 2465
 
2.2%

make_Nissan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35106 
1.0
 
2500

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 1.0

Common Values

Value Count Frequency (%)
0.0 35106
93.4%
1.0 2500
 
6.6%

Length

2024-05-20T00:02:23.111135 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.220471 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35106
93.4%
1.0 2500
 
6.6%

Most occurring characters

Value Count Frequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 72712
64.5%
. 37606
33.3%
1 2500
 
2.2%

make_Porsche
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37405 
1.0
 
201

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37405
99.5%
1.0 201
 
0.5%

Length

2024-05-20T00:02:23.341406 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.450722 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37405
99.5%
1.0 201
 
0.5%

Most occurring characters

Value Count Frequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75011
66.5%
. 37606
33.3%
1 201
 
0.2%

make_RAM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37004 
1.0
 
602

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37004
98.4%
1.0 602
 
1.6%

Length

2024-05-20T00:02:23.574636 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.687520 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37004
98.4%
1.0 602
 
1.6%

Most occurring characters

Value Count Frequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74610
66.1%
. 37606
33.3%
1 602
 
0.5%

make_Subaru
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35952 
1.0
 
1654

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 35952
95.6%
1.0 1654
 
4.4%

Length

2024-05-20T00:02:23.806719 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:23.918280 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35952
95.6%
1.0 1654
 
4.4%

Most occurring characters

Value Count Frequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73558
65.2%
. 37606
33.3%
1 1654
 
1.5%

make_Toyota
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36303 
1.0
 
1303

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36303
96.5%
1.0 1303
 
3.5%

Length

2024-05-20T00:02:24.038033 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:24.201439 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36303
96.5%
1.0 1303
 
3.5%

Most occurring characters

Value Count Frequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73909
65.5%
. 37606
33.3%
1 1303
 
1.2%

make_Volkswagen
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
35402 
1.0
 
2204

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 35402
94.1%
1.0 2204
 
5.9%

Length

2024-05-20T00:02:24.366065 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:24.478456 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 35402
94.1%
1.0 2204
 
5.9%

Most occurring characters

Value Count Frequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73008
64.7%
. 37606
33.3%
1 2204
 
2.0%

make_Volvo
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37257 
1.0
 
349

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37257
99.1%
1.0 349
 
0.9%

Length

2024-05-20T00:02:24.594840 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:24.707198 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37257
99.1%
1.0 349
 
0.9%

Most occurring characters

Value Count Frequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74863
66.4%
. 37606
33.3%
1 349
 
0.3%

bodystyle_Cargo Van
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37231 
1.0
 
375

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37231
99.0%
1.0 375
 
1.0%

Length

2024-05-20T00:02:24.954182 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.071094 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37231
99.0%
1.0 375
 
1.0%

Most occurring characters

Value Count Frequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74837
66.3%
. 37606
33.3%
1 375
 
0.3%

bodystyle_Convertible
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37336 
1.0
 
270

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37336
99.3%
1.0 270
 
0.7%

Length

2024-05-20T00:02:25.188026 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.304526 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37336
99.3%
1.0 270
 
0.7%

Most occurring characters

Value Count Frequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74942
66.4%
. 37606
33.3%
1 270
 
0.2%

bodystyle_Coupe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36556 
1.0
 
1050

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36556
97.2%
1.0 1050
 
2.8%

Length

2024-05-20T00:02:25.426001 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.541347 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36556
97.2%
1.0 1050
 
2.8%

Most occurring characters

Value Count Frequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74162
65.7%
. 37606
33.3%
1 1050
 
0.9%

bodystyle_Hatchback
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37221 
1.0
 
385

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37221
99.0%
1.0 385
 
1.0%

Length

2024-05-20T00:02:25.660549 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:25.773910 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37221
99.0%
1.0 385
 
1.0%

Most occurring characters

Value Count Frequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 74827
66.3%
. 37606
33.3%
1 385
 
0.3%

bodystyle_Minivan
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37571 
1.0
 
35

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37571
99.9%
1.0 35
 
0.1%

Length

2024-05-20T00:02:25.930002 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.066028 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37571
99.9%
1.0 35
 
0.1%

Most occurring characters

Value Count Frequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75177
66.6%
. 37606
33.3%
1 35
 
< 0.1%

bodystyle_Passenger Van
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37454 
1.0
 
152

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37454
99.6%
1.0 152
 
0.4%

Length

2024-05-20T00:02:26.194240 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.335811 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37454
99.6%
1.0 152
 
0.4%

Most occurring characters

Value Count Frequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75060
66.5%
. 37606
33.3%
1 152
 
0.1%

bodystyle_Pickup Truck
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
34422 
1.0
 
3184

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 34422
91.5%
1.0 3184
 
8.5%

Length

2024-05-20T00:02:26.532359 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.648433 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 34422
91.5%
1.0 3184
 
8.5%

Most occurring characters

Value Count Frequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 72028
63.8%
. 37606
33.3%
1 3184
 
2.8%

bodystyle_SUV
Categorical

HIGH CORRELATION 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
1.0
25723 
0.0
11883 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 1.0
3rd row 1.0
4th row 1.0
5th row 1.0

Common Values

Value Count Frequency (%)
1.0 25723
68.4%
0.0 11883
31.6%

Length

2024-05-20T00:02:26.778078 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:26.931878 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
1.0 25723
68.4%
0.0 11883
31.6%

Most occurring characters

Value Count Frequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 49489
43.9%
. 37606
33.3%
1 25723
22.8%

bodystyle_Sedan
Categorical

HIGH CORRELATION 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
31412 
1.0
6194 

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 31412
83.5%
1.0 6194
 
16.5%

Length

2024-05-20T00:02:27.074374 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:27.184073 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 31412
83.5%
1.0 6194
 
16.5%

Most occurring characters

Value Count Frequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 69018
61.2%
. 37606
33.3%
1 6194
 
5.5%

bodystyle_Wagon
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37475 
1.0
 
131

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37475
99.7%
1.0 131
 
0.3%

Length

2024-05-20T00:02:27.317881 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:27.431202 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37475
99.7%
1.0 131
 
0.3%

Most occurring characters

Value Count Frequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75081
66.6%
. 37606
33.3%
1 131
 
0.1%

bodystyle_nan
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37499 
1.0
 
107

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37499
99.7%
1.0 107
 
0.3%

Length

2024-05-20T00:02:27.559019 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:27.685550 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37499
99.7%
1.0 107
 
0.3%

Most occurring characters

Value Count Frequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75105
66.6%
. 37606
33.3%
1 107
 
0.1%

cat_x0
Real number (ℝ)

HIGH CORRELATION 

Distinct 35
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.19077421
Minimum -0.6724633
Maximum 1.0948846
Zeros 0
Zeros (%) 0.0%
Negative 7671
Negative (%) 20.4%
Memory size 293.9 KiB
2024-05-20T00:02:27.838646 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -0.6724633
5-th percentile -0.33562684
Q1 0.030993938
median 0.12328106
Q3 0.2258312
95-th percentile 1.0166872
Maximum 1.0948846
Range 1.7673479
Interquartile range (IQR) 0.19483726

Descriptive statistics

Standard deviation 0.33535404
Coefficient of variation (CV) 1.7578584
Kurtosis 1.2986083
Mean 0.19077421
Median Absolute Deviation (MAD) 0.099178717
Skewness 0.9591552
Sum 7174.2548
Variance 0.11246233
Monotonicity Not monotonic
2024-05-20T00:02:27.999745 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
Value Count Frequency (%)
0.1232810616 8367
22.2%
0.03099393845 5523
14.7%
-0.01229947805 4104
10.9%
0.401217401 3338
 
8.9%
0.2224597782 2306
 
6.1%
0.1999615133 1992
 
5.3%
1.016687155 1679
 
4.5%
0.06438097358 1662
 
4.4%
-0.4631993473 1258
 
3.3%
0.8305656314 1167
 
3.1%
Other values (25) 6210
16.5%
Value Count Frequency (%)
-0.6724632978 86
 
0.2%
-0.4631993473 1258
 
3.3%
-0.3356268406 1008
 
2.7%
-0.2919861972 554
 
1.5%
-0.2653982937 65
 
0.2%
-0.2560895979 138
 
0.4%
-0.05258842185 109
 
0.3%
-0.0498467274 171
 
0.5%
-0.0263671279 178
 
0.5%
-0.01229947805 4104
10.9%
Value Count Frequency (%)
1.094884634 868
2.3%
1.016687155 1679
4.5%
0.9313512444 35
 
0.1%
0.9192990065 21
 
0.1%
0.8305656314 1167
3.1%
0.7679092288 62
 
0.2%
0.7367572188 476
 
1.3%
0.6361443996 97
 
0.3%
0.5120633245 14
 
< 0.1%
0.4917612076 120
 
0.3%

cat_x1
Real number (ℝ)

HIGH CORRELATION 

Distinct 35
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -0.39644773
Minimum -1.9109991
Maximum 0.30777144
Zeros 0
Zeros (%) 0.0%
Negative 37215
Negative (%) 99.0%
Memory size 293.9 KiB
2024-05-20T00:02:28.152449 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -1.9109991
5-th percentile -0.86696702
Q1 -0.38160509
median -0.35996246
Q3 -0.26118255
95-th percentile -0.23218849
Maximum 0.30777144
Range 2.2187705
Interquartile range (IQR) 0.12042254

Descriptive statistics

Standard deviation 0.21295085
Coefficient of variation (CV) -0.53714735
Kurtosis 6.4058537
Mean -0.39644773
Median Absolute Deviation (MAD) 0.061425865
Skewness -1.5724674
Sum -14908.813
Variance 0.045348063
Monotonicity Not monotonic
2024-05-20T00:02:28.299532 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
Value Count Frequency (%)
-0.3816050887 8367
22.2%
-0.2985365987 5523
14.7%
-0.232188493 4104
10.9%
-0.2376151383 3338
 
8.9%
-0.613470912 2306
 
6.1%
-0.5093790293 1992
 
5.3%
-0.8669670224 1679
 
4.5%
-0.3599624634 1662
 
4.4%
-0.3030163944 1258
 
3.3%
-0.7890874743 1167
 
3.1%
Other values (25) 6210
16.5%
Value Count Frequency (%)
-1.91099906 35
 
0.1%
-1.809605837 62
 
0.2%
-1.520576596 17
 
< 0.1%
-1.110056043 120
 
0.3%
-0.8875585794 476
 
1.3%
-0.8669670224 1679
4.5%
-0.7890874743 1167
3.1%
-0.7393200397 868
2.3%
-0.7229655385 97
 
0.3%
-0.6710458398 14
 
< 0.1%
Value Count Frequency (%)
0.3077714443 261
 
0.7%
0.2529188097 21
 
0.1%
0.01071238518 109
 
0.3%
-0.05026694015 138
 
0.4%
-0.1463941783 554
 
1.5%
-0.232188493 4104
10.9%
-0.2376151383 3338
8.9%
-0.2611825466 1008
 
2.7%
-0.2985365987 5523
14.7%
-0.299779892 171
 
0.5%

cat_x2
Real number (ℝ)

HIGH CORRELATION 

Distinct 35
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.84585139
Minimum -0.27193058
Maximum 2.6237638
Zeros 0
Zeros (%) 0.0%
Negative 924
Negative (%) 2.5%
Memory size 293.9 KiB
2024-05-20T00:02:28.434268 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum -0.27193058
5-th percentile 0.2573179
Q1 0.74373335
median 0.7503739
Q3 0.88927448
95-th percentile 1.4716611
Maximum 2.6237638
Range 2.8956944
Interquartile range (IQR) 0.14554113

Descriptive statistics

Standard deviation 0.32237177
Coefficient of variation (CV) 0.38112105
Kurtosis 3.4454284
Mean 0.84585139
Median Absolute Deviation (MAD) 0.061797082
Skewness 0.49445913
Sum 31809.087
Variance 0.10392356
Monotonicity Not monotonic
2024-05-20T00:02:28.601070 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
Value Count Frequency (%)
0.7437333465 8367
22.2%
0.8121709824 5523
14.7%
0.889274478 4104
10.9%
0.7503738999 3338
 
8.9%
0.9181630611 2306
 
6.1%
0.5571174026 1992
 
5.3%
1.471661091 1679
 
4.5%
0.702658534 1662
 
4.4%
1.513098717 1258
 
3.3%
1.447463393 1167
 
3.1%
Other values (25) 6210
16.5%
Value Count Frequency (%)
-0.2719305754 261
 
0.7%
-0.1380209923 109
 
0.3%
-0.001169999479 554
 
1.5%
0.08232649416 138
 
0.4%
0.2573179007 868
2.3%
0.4859781563 17
 
< 0.1%
0.5571174026 1992
5.3%
0.6247327924 24
 
0.1%
0.6732704043 993
2.6%
0.7021681666 368
 
1.0%
Value Count Frequency (%)
2.6237638 35
 
0.1%
2.56569767 62
 
0.2%
1.594357252 178
 
0.5%
1.513098717 1258
3.3%
1.471661091 1679
4.5%
1.465922356 21
 
0.1%
1.447463393 1167
3.1%
1.317383528 120
 
0.3%
1.265677929 1008
2.7%
1.234428525 97
 
0.3%

fuel_type_Electric
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36290 
1.0
 
1316

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1.0
2nd row 0.0
3rd row 1.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36290
96.5%
1.0 1316
 
3.5%

Length

2024-05-20T00:02:28.839072 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:28.945709 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36290
96.5%
1.0 1316
 
3.5%

Most occurring characters

Value Count Frequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73896
65.5%
. 37606
33.3%
1 1316
 
1.2%

fuel_type_Flexible
Categorical

IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
37470 
1.0
 
136

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 0.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 37470
99.6%
1.0 136
 
0.4%

Length

2024-05-20T00:02:29.216566 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:29.330102 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 37470
99.6%
1.0 136
 
0.4%

Most occurring characters

Value Count Frequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 75076
66.5%
. 37606
33.3%
1 136
 
0.1%

fuel_type_Gasoline
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
1.0
34798 
0.0
 
2808

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 1.0
3rd row 0.0
4th row 0.0
5th row 1.0

Common Values

Value Count Frequency (%)
1.0 34798
92.5%
0.0 2808
 
7.5%

Length

2024-05-20T00:02:29.447233 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:29.557917 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
1.0 34798
92.5%
0.0 2808
 
7.5%

Most occurring characters

Value Count Frequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 40414
35.8%
. 37606
33.3%
1 34798
30.8%

fuel_type_Hybrid
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 293.9 KiB
0.0
36250 
1.0
 
1356

Length

Max length 3
Median length 3
Mean length 3
Min length 3

Characters and Unicode

Total characters 112818
Distinct characters 3
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 0.0
2nd row 0.0
3rd row 0.0
4th row 1.0
5th row 0.0

Common Values

Value Count Frequency (%)
0.0 36250
96.4%
1.0 1356
 
3.6%

Length

2024-05-20T00:02:29.703947 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:02:29.838451 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Value Count Frequency (%)
0.0 36250
96.4%
1.0 1356
 
3.6%

Most occurring characters

Value Count Frequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Most occurring categories

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Most occurring scripts

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Most occurring blocks

Value Count Frequency (%)
(unknown) 112818
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
0 73856
65.5%
. 37606
33.3%
1 1356
 
1.2%

Interactions

2024-05-20T00:01:51.838754 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.383948 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.676122 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.783332 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.844504 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.893994 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.374656 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.395891 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.433330 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.702695 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.959275 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.104940 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.240821 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.456331 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.955989 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.863492 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.947281 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.506149 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.805391 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.926827 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.959759 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.164985 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.507766 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.498874 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.692095 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.811721 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.094459 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.214511 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.355445 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.565328 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.070349 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.978006 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.060866 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.613630 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.939893 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.042385 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.120502 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.400746 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.670761 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.655269 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.879167 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.098640 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.209226 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.351102 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.473707 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.676459 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.186903 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.089603 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.170910 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.860225 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.087820 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.151905 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.249297 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.526914 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.779446 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.796042 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.017331 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.312833 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.386544 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.466877 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.590677 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.823032 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.304664 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.198163 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.305484 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:19.970545 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.230533 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.291775 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.354520 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.627555 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.881495 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.911573 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.162676 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.453843 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.577082 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.588426 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.705423 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.946616 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.452018 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.306860 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.496765 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.115020 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.344955 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.396787 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.471446 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.742642 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.029232 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.044359 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.276206 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.566119 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.718309 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.747992 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.826950 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.063457 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.589225 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.428408 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.612287 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.245292 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.482080 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.509752 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.588028 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:28.978705 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.140821 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.175362 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.396862 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.677832 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.854777 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:41.904726 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.993209 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.251397 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.709790 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.540987 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.709808 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.349702 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.586162 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.642563 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.690281 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.091815 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.234655 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.313088 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.505965 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.807413 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:39.983559 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.008181 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.117219 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.382329 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.809896 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.666207 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.832351 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.472850 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.703396 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.780361 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.807922 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.223487 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.378441 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.434572 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.618520 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:37.916650 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.183250 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.158367 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.231238 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.533921 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:48.934480 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.797228 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:52.978674 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.636079 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.820701 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:24.893203 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:26.953851 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.351636 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.484343 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.527811 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.732283 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.027661 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.298187 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.275934 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.445353 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:46.727643 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.048027 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:50.894842 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.099938 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.754367 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:22.948576 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.023499 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.174548 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.484228 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.587820 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.638307 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.859894 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.147783 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.415244 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.401569 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.716101 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.080559 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.170205 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.011679 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.214534 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:20.878697 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.123894 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.150594 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.306660 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.620311 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.803646 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.752922 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:35.982565 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.284654 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.536927 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.531178 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.839643 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.222086 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.290348 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.126259 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.324126 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.000621 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.239724 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.274320 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.421441 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.780085 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:31.915845 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.862710 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.110430 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.398041 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.656467 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.647766 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:44.951854 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.339659 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.407923 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.232880 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.430707 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.154883 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.361560 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.394910 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.542896 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:29.892598 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.060251 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:33.965972 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.286992 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.509086 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.767602 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:42.881990 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.079792 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.452232 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.523525 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.339435 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.544245 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.295368 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.487684 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.618855 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.667249 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.137880 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.174481 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.187633 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.484637 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.622946 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.883825 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.001706 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.196383 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.742837 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.643538 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.456008 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:53.646801 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:21.543142 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:23.601861 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:25.729620 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:27.778969 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:30.262962 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:32.283741 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:34.308612 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:36.594089 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:38.726632 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:40.993332 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:43.125265 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:45.341972 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:47.844421 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:49.750906 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:01:51.665083 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-20T00:02:30.088040 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
bodystyle_Cargo Van bodystyle_Convertible bodystyle_Coupe bodystyle_Hatchback bodystyle_Minivan bodystyle_Passenger Van bodystyle_Pickup Truck bodystyle_SUV bodystyle_Sedan bodystyle_Wagon bodystyle_nan cat_x0 cat_x1 cat_x2 drivetrain_All-wheel Drive drivetrain_Front-wheel Drive drivetrain_Rear-wheel Drive exterior_color_x0 exterior_color_x1 exterior_color_x2 exterior_color_x3 exterior_color_x4 fuel_type_Electric fuel_type_Flexible fuel_type_Gasoline fuel_type_Hybrid interior_color_x0 interior_color_x1 interior_color_x2 interior_color_x3 interior_color_x4 make_Acura make_Audi make_BMW make_Buick make_Cadillac make_Chevrolet make_Dodge make_Ford make_GMC make_Honda make_Hyundai make_INFINITI make_Jeep make_Kia make_Land Rover make_Lexus make_Lincoln make_Mazda make_Mercedes-Benz make_Nissan make_Porsche make_RAM make_Subaru make_Toyota make_Volkswagen make_Volvo mileage model_hashed_0 model_hashed_1 model_hashed_10 model_hashed_11 model_hashed_12 model_hashed_13 model_hashed_14 model_hashed_15 model_hashed_16 model_hashed_17 model_hashed_18 model_hashed_19 model_hashed_2 model_hashed_20 model_hashed_21 model_hashed_22 model_hashed_23 model_hashed_24 model_hashed_25 model_hashed_26 model_hashed_27 model_hashed_28 model_hashed_29 model_hashed_3 model_hashed_30 model_hashed_31 model_hashed_32 model_hashed_33 model_hashed_34 model_hashed_35 model_hashed_36 model_hashed_37 model_hashed_38 model_hashed_39 model_hashed_4 model_hashed_40 model_hashed_41 model_hashed_42 model_hashed_43 model_hashed_44 model_hashed_45 model_hashed_46 model_hashed_47 model_hashed_48 model_hashed_49 model_hashed_5 model_hashed_50 model_hashed_51 model_hashed_6 model_hashed_7 model_hashed_8 model_hashed_9 msrp stock_type year
bodystyle_Cargo Van 1.000 0.005 0.015 0.007 0.000 0.000 0.030 0.147 0.044 0.000 0.000 0.137 -0.171 0.119 0.136 0.005 0.273 0.025 -0.019 -0.019 0.045 -0.052 0.018 0.000 0.028 0.018 -0.009 -0.025 0.019 0.051 -0.050 0.012 0.017 0.022 0.009 0.017 0.021 0.016 0.047 0.013 0.018 0.025 0.010 0.028 0.016 0.009 0.013 0.012 0.017 0.218 0.012 0.002 0.067 0.020 0.018 0.024 0.007 -0.021 0.016 0.027 0.005 0.005 0.031 0.012 0.008 0.058 0.012 0.028 0.012 0.014 0.009 0.019 0.009 0.015 0.067 0.010 0.076 0.007 0.019 0.014 0.019 0.024 0.014 0.006 0.012 0.013 0.011 0.006 0.000 0.015 0.010 0.028 0.005 0.015 0.002 0.005 0.009 0.000 0.010 0.010 0.006 0.022 0.006 0.010 0.010 0.015 0.006 0.444 0.000 0.014 0.078 0.026 0.023
bodystyle_Convertible 0.005 1.000 0.012 0.005 0.000 0.000 0.025 0.125 0.037 0.000 0.000 -0.007 0.026 -0.033 0.046 0.031 0.162 -0.016 -0.004 0.009 0.004 0.011 0.014 0.000 0.023 0.015 -0.017 -0.011 0.009 0.020 -0.022 0.010 0.043 0.137 0.011 0.014 0.014 0.013 0.000 0.011 0.014 0.021 0.008 0.019 0.013 0.006 0.010 0.009 0.030 0.035 0.019 0.000 0.008 0.017 0.013 0.016 0.000 0.040 0.013 0.006 0.000 0.129 0.016 0.016 0.017 0.000 0.015 0.049 0.009 0.015 0.000 0.012 0.006 0.009 0.011 0.008 0.007 0.016 0.006 0.011 0.014 0.007 0.039 0.004 0.010 0.010 0.008 0.003 0.079 0.008 0.008 0.062 0.017 0.012 0.000 0.003 0.007 0.135 0.000 0.021 0.035 0.015 0.000 0.008 0.014 0.064 0.022 0.013 0.109 0.011 0.043 0.054 -0.056
bodystyle_Coupe 0.015 0.012 1.000 0.016 0.000 0.008 0.051 0.249 0.075 0.007 0.005 -0.065 0.160 -0.172 0.169 0.072 0.500 -0.003 -0.001 -0.021 0.029 0.007 0.031 0.007 0.044 0.027 -0.025 -0.006 0.027 0.040 -0.046 0.020 0.044 0.096 0.014 0.022 0.019 0.170 0.015 0.025 0.022 0.035 0.000 0.049 0.029 0.018 0.019 0.022 0.027 0.012 0.016 0.053 0.020 0.034 0.006 0.040 0.015 0.087 0.055 0.016 0.012 0.055 0.033 0.028 0.022 0.061 0.055 0.157 0.023 0.024 0.010 0.025 0.010 0.010 0.018 0.056 0.019 0.007 0.010 0.021 0.022 0.008 0.018 0.007 0.000 0.025 0.019 0.041 0.028 0.109 0.021 0.015 0.017 0.027 0.010 0.012 0.006 0.061 0.016 0.003 0.126 0.033 0.045 0.018 0.007 0.009 0.000 0.217 0.089 0.019 0.102 0.114 -0.148
bodystyle_Hatchback 0.007 0.005 0.016 1.000 0.000 0.000 0.030 0.149 0.045 0.000 0.000 0.084 -0.116 -0.008 0.021 0.033 0.019 0.012 -0.006 0.006 -0.024 0.017 0.018 0.000 0.026 0.015 -0.017 -0.011 0.028 0.041 -0.049 0.070 0.134 0.013 0.009 0.017 0.022 0.016 0.023 0.014 0.015 0.026 0.010 0.029 0.015 0.009 0.013 0.012 0.016 0.000 0.020 0.002 0.011 0.074 0.015 0.022 0.007 0.006 0.047 0.031 0.010 0.005 0.019 0.014 0.017 0.015 0.012 0.009 0.012 0.004 0.038 0.019 0.009 0.011 0.019 0.007 0.004 0.003 0.020 0.011 0.019 0.013 0.015 0.251 0.013 0.013 0.011 0.006 0.000 0.008 0.030 0.025 0.049 0.015 0.002 0.092 0.010 0.000 0.007 0.071 0.006 0.022 0.004 0.000 0.010 0.088 0.007 0.009 0.157 0.014 -0.004 0.004 -0.004
bodystyle_Minivan 0.000 0.000 0.000 0.000 1.000 0.000 0.006 0.044 0.011 0.000 0.000 0.041 -0.053 0.041 0.047 0.054 0.000 0.005 -0.006 0.012 -0.013 0.009 0.000 0.000 0.000 0.000 -0.015 0.008 0.008 -0.015 0.014 0.000 0.000 0.000 0.007 0.000 0.007 0.000 0.000 0.000 0.082 0.004 0.000 0.005 0.037 0.000 0.000 0.000 0.000 0.004 0.004 0.000 0.000 0.000 0.003 0.003 0.000 -0.026 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.139 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.015 0.000 0.014 0.000 0.000 0.012 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.055 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -0.002 0.019 0.016
bodystyle_Passenger Van 0.000 0.000 0.008 0.000 0.000 1.000 0.018 0.093 0.027 0.000 0.000 0.086 -0.109 0.081 0.109 0.056 0.119 0.001 0.012 0.011 -0.010 0.016 0.010 0.000 0.015 0.007 0.006 -0.025 -0.005 0.028 -0.022 0.006 0.009 0.013 0.001 0.009 0.020 0.025 0.017 0.007 0.094 0.015 0.004 0.017 0.023 0.001 0.006 0.005 0.000 0.096 0.015 0.000 0.004 0.012 0.004 0.014 0.000 -0.007 0.008 0.264 0.003 0.000 0.011 0.011 0.008 0.000 0.005 0.006 0.005 0.009 0.000 0.011 0.000 0.035 0.166 0.003 0.002 0.000 0.011 0.007 0.010 0.009 0.007 0.000 0.000 0.006 0.004 0.000 0.000 0.008 0.002 0.003 0.000 0.007 0.000 0.000 0.002 0.000 0.003 0.003 0.000 0.022 0.000 0.003 0.003 0.008 0.000 0.104 0.003 0.007 0.014 0.008 -0.007
bodystyle_Pickup Truck 0.030 0.025 0.051 0.030 0.006 0.018 1.000 0.447 0.135 0.016 0.014 0.306 -0.383 0.099 0.142 0.143 0.018 -0.002 0.048 -0.048 0.069 -0.042 0.051 0.000 0.055 0.028 0.074 0.035 -0.042 0.066 -0.065 0.043 0.056 0.069 0.033 0.055 0.192 0.046 0.120 0.215 0.041 0.032 0.037 0.032 0.054 0.033 0.043 0.042 0.056 0.077 0.021 0.021 0.383 0.065 0.025 0.076 0.028 -0.014 0.393 0.031 0.070 0.024 0.061 0.105 0.044 0.025 0.125 0.039 0.042 0.079 0.034 0.049 0.034 0.049 0.048 0.032 0.039 0.030 0.056 0.193 0.060 0.043 0.271 0.016 0.342 0.044 0.026 0.027 0.020 0.046 0.036 0.019 0.026 0.049 0.021 0.027 0.035 0.030 0.032 0.165 0.028 0.066 0.123 0.056 0.036 0.041 0.028 0.031 0.034 0.047 0.170 0.017 0.023
bodystyle_SUV 0.147 0.125 0.249 0.149 0.044 0.093 0.447 1.000 0.653 0.086 0.078 -0.433 0.577 0.001 0.323 0.212 0.266 0.004 -0.015 -0.017 -0.043 -0.020 0.096 0.016 0.117 0.065 -0.066 0.056 0.062 -0.096 0.085 0.011 0.063 0.088 0.051 0.058 0.070 0.009 0.011 0.053 0.007 0.034 0.031 0.164 0.044 0.074 0.014 0.074 0.083 0.112 0.070 0.023 0.187 0.070 0.039 0.026 0.002 -0.143 0.194 0.082 0.056 0.042 0.123 0.099 0.052 0.075 0.191 0.097 0.080 0.139 0.122 0.114 0.027 0.086 0.121 0.083 0.050 0.065 0.103 0.141 0.117 0.080 0.154 0.044 0.151 0.082 0.130 0.045 0.055 0.085 0.096 0.000 0.100 0.084 0.026 0.029 0.076 0.054 0.144 0.114 0.022 0.140 0.067 0.011 0.080 0.058 0.013 0.186 0.054 0.086 0.013 0.163 0.186
bodystyle_Sedan 0.044 0.037 0.075 0.045 0.011 0.027 0.135 0.653 1.000 0.025 0.022 0.256 -0.398 -0.054 0.359 0.384 0.000 -0.010 -0.011 0.060 -0.013 0.053 0.047 0.011 0.056 0.029 0.046 -0.079 -0.068 0.020 -0.007 0.013 0.052 0.097 0.027 0.002 0.039 0.018 0.114 0.067 0.036 0.024 0.000 0.130 0.004 0.049 0.058 0.035 0.053 0.109 0.132 0.030 0.056 0.030 0.031 0.039 0.032 0.138 0.069 0.051 0.036 0.033 0.083 0.080 0.050 0.094 0.159 0.045 0.044 0.139 0.167 0.078 0.030 0.068 0.102 0.089 0.052 0.057 0.066 0.049 0.073 0.128 0.046 0.000 0.059 0.052 0.175 0.053 0.062 0.073 0.138 0.007 0.118 0.075 0.009 0.017 0.052 0.013 0.196 0.089 0.042 0.099 0.028 0.049 0.087 0.056 0.032 0.136 0.046 0.093 -0.216 0.154 -0.163
bodystyle_Wagon 0.000 0.000 0.007 0.000 0.000 0.000 0.016 0.086 0.025 1.000 0.000 0.062 -0.077 0.077 0.012 0.020 0.010 -0.010 0.005 0.031 -0.003 0.024 0.009 0.000 0.015 0.009 0.012 0.002 -0.022 0.010 -0.003 0.005 0.068 0.009 0.000 0.008 0.018 0.007 0.011 0.005 0.009 0.014 0.002 0.016 0.008 0.000 0.005 0.004 0.008 0.001 0.000 0.000 0.003 0.000 0.000 0.074 0.076 0.060 0.009 0.027 0.043 0.067 0.010 0.010 0.007 0.028 0.003 0.000 0.004 0.006 0.000 0.010 0.000 0.016 0.003 0.001 0.000 0.000 0.010 0.005 0.009 0.015 0.034 0.000 0.223 0.005 0.002 0.046 0.000 0.007 0.044 0.000 0.050 0.007 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.001 0.017 0.006 0.005 0.019 0.000 0.006 -0.026 0.055 -0.076
bodystyle_nan 0.000 0.000 0.005 0.000 0.000 0.000 0.014 0.078 0.022 0.000 1.000 0.005 -0.018 0.015 0.000 0.000 0.000 0.012 -0.007 -0.002 -0.000 -0.002 0.007 0.000 0.013 0.007 0.007 0.003 -0.012 -0.017 0.028 0.018 0.000 0.010 0.000 0.014 0.016 0.000 0.004 0.004 0.003 0.012 0.000 0.014 0.006 0.001 0.003 0.002 0.007 0.000 0.001 0.000 0.022 0.006 0.003 0.077 0.000 0.013 0.000 0.000 0.036 0.000 0.004 0.005 0.007 0.009 0.000 0.000 0.000 0.004 0.000 0.008 0.000 0.000 0.047 0.000 0.000 0.000 0.000 0.004 0.008 0.003 0.000 0.000 0.013 0.045 0.000 0.000 0.000 0.005 0.016 0.000 0.010 0.002 0.047 0.000 0.000 0.000 0.000 0.000 0.000 0.054 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 -0.009 0.000 -0.013
cat_x0 0.137 -0.007 -0.065 0.084 0.041 0.086 0.306 -0.433 0.256 0.062 0.005 1.000 -0.620 -0.160 0.397 0.373 0.537 -0.002 0.050 -0.012 0.028 -0.006 0.338 0.104 0.400 0.480 0.058 -0.094 -0.066 0.053 -0.043 0.174 0.233 0.308 0.148 0.163 0.164 0.459 0.207 0.210 0.151 0.203 0.141 0.454 0.116 0.178 0.157 0.164 0.211 0.402 0.243 0.085 0.400 0.142 0.133 0.224 0.108 0.015 0.304 0.132 0.163 0.079 0.334 0.189 0.131 0.119 0.116 0.185 0.135 0.109 0.225 0.192 0.129 0.168 0.163 0.177 0.128 0.105 0.201 0.124 0.145 0.095 0.214 0.076 0.274 0.121 0.102 0.149 0.088 0.174 0.106 0.098 0.263 0.092 0.094 0.120 0.118 0.106 0.094 0.149 0.121 0.165 0.121 0.134 0.121 0.161 0.103 0.378 0.151 0.116 0.146 0.217 -0.027
cat_x1 -0.171 0.026 0.160 -0.116 -0.053 -0.109 -0.383 0.577 -0.398 -0.077 -0.018 -0.620 1.000 0.012 0.350 0.395 0.338 -0.033 -0.001 -0.031 0.015 -0.039 0.107 0.177 0.178 0.141 0.003 0.050 -0.022 -0.078 0.093 0.122 0.218 0.199 0.098 0.147 0.163 0.123 0.098 0.185 0.224 0.194 0.145 0.139 0.133 0.117 0.161 0.129 0.199 0.320 0.239 0.132 0.318 0.114 0.055 0.256 0.107 0.005 0.238 0.074 0.088 0.141 0.091 0.119 0.112 0.157 0.099 0.063 0.095 0.194 0.228 0.140 0.059 0.120 0.208 0.141 0.077 0.078 0.114 0.139 0.121 0.077 0.187 0.053 0.282 0.101 0.102 0.073 0.095 0.178 0.110 0.048 0.129 0.044 0.064 0.042 0.111 0.145 0.079 0.141 0.084 0.162 0.114 0.111 0.115 0.106 0.079 0.153 0.079 0.064 0.028 0.174 0.003
cat_x2 0.119 -0.033 -0.172 -0.008 0.041 0.081 0.099 0.001 -0.054 0.077 0.015 -0.160 0.012 1.000 0.434 0.435 0.580 -0.019 -0.007 0.006 0.086 0.011 0.121 0.083 0.294 0.449 -0.005 0.035 -0.027 0.049 -0.013 0.101 0.168 0.228 0.085 0.116 0.275 0.312 0.168 0.268 0.092 0.112 0.088 0.201 0.070 0.099 0.187 0.106 0.098 0.181 0.196 0.073 0.268 0.157 0.082 0.208 0.143 0.063 0.180 0.049 0.175 0.096 0.269 0.178 0.080 0.115 0.053 0.162 0.158 0.145 0.207 0.103 0.059 0.192 0.126 0.129 0.130 0.080 0.131 0.121 0.117 0.195 0.143 0.105 0.264 0.089 0.238 0.111 0.079 0.122 0.095 0.060 0.181 0.099 0.067 0.147 0.076 0.068 0.250 0.306 0.137 0.155 0.117 0.097 0.125 0.071 0.093 0.277 0.102 0.091 0.275 0.170 -0.033
drivetrain_All-wheel Drive 0.136 0.046 0.169 0.021 0.047 0.109 0.142 0.323 0.359 0.012 0.000 0.397 0.350 0.434 1.000 0.883 0.376 0.010 0.053 -0.008 0.032 0.008 0.060 0.011 0.079 0.048 -0.083 -0.027 0.029 0.056 -0.032 0.000 0.095 0.072 0.080 0.033 0.218 0.041 0.018 0.021 0.055 0.076 0.056 0.134 0.052 0.062 0.017 0.042 0.055 0.064 0.150 0.021 0.045 0.119 0.046 0.011 0.036 -0.103 0.078 0.092 0.046 0.034 0.078 0.149 0.044 0.085 0.048 0.039 0.058 0.099 0.045 0.089 0.017 0.073 0.075 0.000 0.028 0.015 0.059 0.027 0.031 0.121 0.057 0.069 0.058 0.065 0.139 0.036 0.015 0.139 0.076 0.031 0.031 0.046 0.031 0.029 0.059 0.019 0.156 0.111 0.061 0.092 0.027 0.192 0.031 0.059 0.031 0.196 0.034 0.065 0.484 0.123 0.164
drivetrain_Front-wheel Drive 0.005 0.031 0.072 0.033 0.054 0.056 0.143 0.212 0.384 0.020 0.000 0.373 0.395 0.435 0.883 1.000 0.103 -0.015 -0.056 0.020 -0.057 -0.001 0.068 0.006 0.072 0.031 0.089 0.037 -0.039 -0.098 0.071 0.010 0.084 0.109 0.096 0.048 0.218 0.055 0.039 0.010 0.079 0.104 0.054 0.116 0.073 0.055 0.013 0.038 0.051 0.122 0.178 0.035 0.036 0.105 0.050 0.035 0.029 0.074 0.076 0.044 0.042 0.026 0.078 0.152 0.060 0.097 0.054 0.056 0.060 0.119 0.043 0.079 0.017 0.071 0.084 0.013 0.026 0.012 0.046 0.021 0.050 0.140 0.065 0.078 0.061 0.054 0.157 0.035 0.033 0.106 0.086 0.028 0.033 0.054 0.025 0.029 0.056 0.027 0.163 0.128 0.004 0.111 0.024 0.217 0.035 0.060 0.026 0.134 0.027 0.057 -0.565 0.090 -0.108
drivetrain_Rear-wheel Drive 0.273 0.162 0.500 0.019 0.000 0.119 0.018 0.266 0.000 0.010 0.000 0.537 0.338 0.580 0.376 0.103 1.000 0.010 -0.001 -0.022 0.045 -0.015 0.004 0.008 0.026 0.040 -0.000 -0.016 0.015 0.073 -0.073 0.029 0.034 0.060 0.020 0.023 0.030 0.197 0.037 0.022 0.039 0.043 0.011 0.054 0.033 0.022 0.009 0.013 0.015 0.106 0.032 0.023 0.024 0.043 0.000 0.043 0.019 0.072 0.020 0.109 0.017 0.026 0.010 0.023 0.131 0.010 0.000 0.149 0.017 0.025 0.013 0.032 0.007 0.016 0.027 0.019 0.019 0.010 0.037 0.026 0.033 0.025 0.008 0.007 0.000 0.029 0.026 0.020 0.061 0.086 0.004 0.009 0.014 0.034 0.014 0.008 0.013 0.065 0.015 0.017 0.115 0.039 0.016 0.021 0.012 0.027 0.014 0.271 0.051 0.029 0.088 0.084 -0.133
exterior_color_x0 0.025 -0.016 -0.003 0.012 0.005 0.001 -0.002 0.004 -0.010 -0.010 0.012 -0.002 -0.033 -0.019 0.010 -0.015 0.010 1.000 0.275 -0.558 0.227 -0.256 0.100 0.041 0.077 0.055 -0.021 -0.038 -0.012 0.021 -0.003 0.069 0.056 0.133 0.090 0.329 0.213 0.159 0.121 0.090 0.102 0.133 0.054 0.264 0.177 0.073 0.091 0.059 0.130 0.101 0.108 0.054 0.177 0.149 0.068 0.199 0.041 -0.031 0.050 0.045 0.030 0.023 0.120 0.070 0.042 0.039 0.048 0.043 0.062 0.044 0.048 0.098 0.047 0.069 0.036 0.071 0.041 0.021 0.104 0.059 0.062 0.047 0.081 0.031 0.065 0.072 0.040 0.040 0.036 0.053 0.039 0.025 0.030 0.059 0.120 0.031 0.077 0.038 0.026 0.050 0.023 0.086 0.060 0.026 0.037 0.125 0.021 0.061 0.121 0.061 0.005 0.118 0.033
exterior_color_x1 -0.019 -0.004 -0.001 -0.006 -0.006 0.012 0.048 -0.015 -0.011 0.005 -0.007 0.050 -0.001 -0.007 0.053 -0.056 -0.001 0.275 1.000 -0.110 -0.058 0.098 0.059 0.043 0.058 0.052 0.017 -0.049 -0.030 0.110 -0.105 0.097 0.087 0.138 0.089 0.083 0.135 0.060 0.123 0.082 0.163 0.106 0.088 0.298 0.187 0.088 0.113 0.068 0.149 0.143 0.122 0.039 0.148 0.174 0.185 0.303 0.035 0.009 0.035 0.048 0.035 0.021 0.104 0.053 0.049 0.040 0.042 0.094 0.030 0.042 0.058 0.111 0.023 0.092 0.043 0.038 0.046 0.029 0.079 0.038 0.052 0.040 0.083 0.035 0.036 0.106 0.065 0.017 0.029 0.076 0.041 0.036 0.038 0.055 0.033 0.035 0.088 0.078 0.056 0.045 0.026 0.117 0.052 0.032 0.038 0.048 0.010 0.066 0.030 0.050 0.063 0.102 -0.002
exterior_color_x2 -0.019 0.009 -0.021 0.006 0.012 0.011 -0.048 -0.017 0.060 0.031 -0.002 -0.012 -0.031 0.006 -0.008 0.020 -0.022 -0.558 -0.110 1.000 -0.234 0.510 0.070 0.022 0.068 0.054 -0.006 0.043 0.020 -0.006 -0.007 0.085 0.047 0.101 0.082 0.109 0.170 0.177 0.122 0.070 0.144 0.065 0.074 0.306 0.134 0.088 0.080 0.140 0.170 0.170 0.115 0.038 0.170 0.122 0.154 0.163 0.028 0.025 0.034 0.062 0.045 0.016 0.135 0.063 0.051 0.050 0.046 0.057 0.038 0.040 0.062 0.106 0.041 0.071 0.049 0.039 0.021 0.041 0.076 0.024 0.055 0.047 0.089 0.040 0.057 0.065 0.062 0.024 0.048 0.057 0.031 0.034 0.029 0.045 0.044 0.029 0.090 0.118 0.048 0.037 0.024 0.063 0.062 0.053 0.041 0.056 0.033 0.047 0.060 0.065 -0.053 0.119 -0.023
exterior_color_x3 0.045 0.004 0.029 -0.024 -0.013 -0.010 0.069 -0.043 -0.013 -0.003 -0.000 0.028 0.015 0.086 0.032 -0.057 0.045 0.227 -0.058 -0.234 1.000 -0.307 0.068 0.012 0.045 0.021 0.013 -0.028 -0.019 0.045 -0.027 0.067 0.062 0.085 0.224 0.092 0.094 0.128 0.080 0.139 0.105 0.121 0.075 0.486 0.183 0.038 0.061 0.044 0.157 0.151 0.068 0.043 0.189 0.109 0.089 0.137 0.030 0.032 0.043 0.031 0.067 0.022 0.144 0.049 0.041 0.041 0.022 0.048 0.036 0.065 0.055 0.123 0.016 0.059 0.050 0.054 0.023 0.052 0.069 0.054 0.087 0.042 0.082 0.017 0.049 0.052 0.045 0.020 0.053 0.092 0.038 0.034 0.036 0.070 0.031 0.033 0.091 0.120 0.035 0.034 0.015 0.049 0.056 0.029 0.097 0.048 0.020 0.054 0.022 0.039 0.113 0.096 -0.034
exterior_color_x4 -0.052 0.011 0.007 0.017 0.009 0.016 -0.042 -0.020 0.053 0.024 -0.002 -0.006 -0.039 0.011 0.008 -0.001 -0.015 -0.256 0.098 0.510 -0.307 1.000 0.066 0.031 0.045 0.043 0.043 -0.046 -0.037 -0.003 0.024 0.105 0.077 0.073 0.098 0.332 0.137 0.146 0.135 0.112 0.111 0.055 0.091 0.269 0.095 0.058 0.190 0.065 0.127 0.135 0.153 0.052 0.143 0.210 0.136 0.118 0.043 0.014 0.043 0.038 0.037 0.018 0.120 0.044 0.062 0.049 0.064 0.040 0.040 0.047 0.055 0.092 0.053 0.110 0.039 0.035 0.028 0.029 0.064 0.069 0.065 0.048 0.063 0.030 0.045 0.045 0.052 0.027 0.018 0.053 0.031 0.031 0.026 0.035 0.163 0.031 0.056 0.026 0.045 0.038 0.025 0.051 0.057 0.051 0.054 0.071 0.034 0.061 0.123 0.068 -0.012 0.119 -0.020
fuel_type_Electric 0.018 0.014 0.031 0.018 0.000 0.010 0.051 0.096 0.047 0.009 0.007 0.338 0.107 0.121 0.060 0.068 0.004 0.100 0.059 0.070 0.068 0.066 1.000 0.009 0.670 0.036 -0.015 -0.023 0.023 0.035 -0.030 0.026 0.016 0.027 0.020 0.122 0.016 0.032 0.046 0.000 0.036 0.000 0.023 0.055 0.072 0.020 0.024 0.026 0.035 0.053 0.000 0.007 0.023 0.036 0.027 0.031 0.000 -0.108 0.032 0.059 0.022 0.023 0.038 0.032 0.031 0.015 0.025 0.025 0.027 0.025 0.021 0.038 0.143 0.030 0.197 0.114 0.018 0.018 0.037 0.021 0.033 0.141 0.018 0.019 0.026 0.015 0.023 0.056 0.011 0.026 0.019 0.011 0.016 0.029 0.012 0.010 0.021 0.221 0.015 0.022 0.017 0.089 0.017 0.023 0.018 0.218 0.017 0.246 0.022 0.029 0.198 0.119 0.088
fuel_type_Flexible 0.000 0.000 0.007 0.000 0.000 0.000 0.000 0.016 0.011 0.000 0.000 0.104 0.177 0.083 0.011 0.006 0.008 0.041 0.043 0.022 0.012 0.031 0.009 1.000 0.211 0.009 -0.013 0.003 0.022 0.011 -0.014 0.005 0.000 0.012 0.000 0.015 0.000 0.008 0.080 0.006 0.009 0.014 0.003 0.003 0.008 0.000 0.005 0.000 0.009 0.000 0.014 0.000 0.003 0.011 0.009 0.013 0.000 0.082 0.019 0.000 0.014 0.000 0.028 0.014 0.000 0.000 0.000 0.005 0.005 0.006 0.007 0.007 0.000 0.039 0.006 0.030 0.000 0.000 0.061 0.023 0.004 0.005 0.006 0.000 0.005 0.005 0.003 0.000 0.000 0.015 0.000 0.002 0.000 0.007 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.004 0.000 0.002 0.017 0.007 0.000 0.008 0.000 0.006 -0.024 0.066 -0.094
fuel_type_Gasoline 0.028 0.023 0.044 0.026 0.000 0.015 0.055 0.117 0.056 0.015 0.013 0.400 0.178 0.294 0.079 0.072 0.026 0.077 0.058 0.068 0.045 0.045 0.670 0.211 1.000 0.681 0.025 0.038 -0.028 -0.064 0.058 0.034 0.027 0.007 0.029 0.064 0.032 0.000 0.058 0.022 0.000 0.019 0.033 0.042 0.060 0.031 0.010 0.000 0.013 0.014 0.037 0.000 0.035 0.057 0.088 0.059 0.054 0.108 0.026 0.068 0.047 0.045 0.048 0.049 0.041 0.019 0.035 0.078 0.033 0.040 0.017 0.043 0.093 0.045 0.124 0.073 0.035 0.039 0.029 0.016 0.016 0.088 0.027 0.029 0.129 0.068 0.025 0.030 0.012 0.027 0.022 0.021 0.005 0.025 0.020 0.009 0.066 0.190 0.029 0.024 0.026 0.057 0.022 0.030 0.026 0.135 0.067 0.154 0.032 0.089 -0.203 0.116 -0.090
fuel_type_Hybrid 0.018 0.015 0.027 0.015 0.000 0.007 0.028 0.065 0.029 0.009 0.007 0.480 0.141 0.449 0.048 0.031 0.040 0.055 0.052 0.054 0.021 0.043 0.036 0.009 0.681 1.000 -0.016 -0.032 0.010 0.052 -0.048 0.019 0.020 0.009 0.020 0.034 0.063 0.030 0.009 0.029 0.042 0.029 0.021 0.005 0.015 0.020 0.044 0.025 0.059 0.031 0.051 0.007 0.024 0.040 0.156 0.048 0.083 -0.072 0.011 0.041 0.087 0.063 0.039 0.038 0.025 0.021 0.030 0.121 0.021 0.030 0.014 0.023 0.008 0.039 0.026 0.018 0.042 0.053 0.037 0.021 0.053 0.028 0.020 0.029 0.190 0.099 0.007 0.016 0.027 0.023 0.031 0.016 0.000 0.060 0.012 0.016 0.091 0.051 0.022 0.024 0.017 0.032 0.011 0.019 0.033 0.026 0.102 0.022 0.022 0.148 0.098 0.067 0.071
interior_color_x0 -0.009 -0.017 -0.025 -0.017 -0.015 0.006 0.074 -0.066 0.046 0.012 0.007 0.058 0.003 -0.005 -0.083 0.089 -0.000 -0.021 0.017 -0.006 0.013 0.043 -0.015 -0.013 0.025 -0.016 1.000 -0.657 -0.805 0.264 -0.079 0.113 0.093 0.081 0.050 0.151 0.426 0.092 0.149 0.246 0.093 0.183 0.170 0.164 0.086 0.040 0.054 0.102 0.100 0.099 0.091 0.033 0.087 0.129 0.078 0.102 0.063 -0.055 0.045 0.035 0.047 0.025 0.063 0.136 0.059 0.028 0.042 0.040 0.040 0.095 0.047 0.063 0.025 0.088 0.050 0.030 0.049 0.035 0.086 0.069 0.059 0.028 0.268 0.052 0.095 0.031 0.057 0.094 0.048 0.086 0.044 0.025 0.043 0.077 0.068 0.032 0.042 0.024 0.029 0.026 0.059 0.116 0.035 0.051 0.120 0.046 0.037 0.055 0.077 0.069 0.004 0.078 0.043
interior_color_x1 -0.025 -0.011 -0.006 -0.011 0.008 -0.025 0.035 0.056 -0.079 0.002 0.003 -0.094 0.050 0.035 -0.027 0.037 -0.016 -0.038 -0.049 0.043 -0.028 -0.046 -0.023 0.003 0.038 -0.032 -0.657 1.000 0.612 -0.449 0.256 0.195 0.124 0.138 0.222 0.158 0.450 0.157 0.300 0.251 0.115 0.180 0.169 0.208 0.130 0.149 0.086 0.125 0.086 0.153 0.499 0.048 0.098 0.119 0.142 0.142 0.204 0.055 0.074 0.055 0.066 0.036 0.100 0.186 0.161 0.044 0.104 0.066 0.133 0.094 0.044 0.125 0.096 0.062 0.047 0.048 0.055 0.053 0.119 0.059 0.126 0.123 0.273 0.064 0.090 0.077 0.052 0.053 0.033 0.111 0.049 0.030 0.034 0.082 0.057 0.051 0.047 0.041 0.071 0.037 0.069 0.126 0.042 0.130 0.119 0.083 0.042 0.079 0.048 0.068 -0.095 0.111 -0.064
interior_color_x2 0.019 0.009 0.027 0.028 0.008 -0.005 -0.042 0.062 -0.068 -0.022 -0.012 -0.066 -0.022 -0.027 0.029 -0.039 0.015 -0.012 -0.030 0.020 -0.019 -0.037 0.023 0.022 -0.028 0.010 -0.805 0.612 1.000 -0.233 0.007 0.270 0.131 0.148 0.263 0.106 0.364 0.159 0.404 0.201 0.133 0.288 0.242 0.332 0.135 0.229 0.124 0.119 0.101 0.191 0.355 0.047 0.130 0.201 0.093 0.096 0.129 0.047 0.048 0.068 0.053 0.047 0.161 0.150 0.142 0.062 0.069 0.060 0.187 0.094 0.098 0.192 0.088 0.098 0.072 0.050 0.068 0.044 0.163 0.056 0.191 0.105 0.134 0.071 0.085 0.051 0.067 0.047 0.034 0.086 0.176 0.069 0.067 0.085 0.046 0.052 0.056 0.036 0.057 0.064 0.050 0.146 0.045 0.103 0.103 0.101 0.039 0.061 0.048 0.099 -0.059 0.103 -0.041
interior_color_x3 0.051 0.020 0.040 0.041 -0.015 0.028 0.066 -0.096 0.020 0.010 -0.017 0.053 -0.078 0.049 0.056 -0.098 0.073 0.021 0.110 -0.006 0.045 -0.003 0.035 0.011 -0.064 0.052 0.264 -0.449 -0.233 1.000 -0.894 0.279 0.131 0.150 0.205 0.091 0.321 0.162 0.453 0.181 0.133 0.291 0.272 0.184 0.151 0.203 0.074 0.156 0.116 0.115 0.502 0.045 0.141 0.203 0.039 0.105 0.152 0.028 0.076 0.074 0.041 0.042 0.094 0.150 0.211 0.057 0.102 0.069 0.175 0.104 0.063 0.138 0.107 0.093 0.075 0.055 0.077 0.043 0.170 0.057 0.191 0.121 0.211 0.068 0.082 0.041 0.115 0.037 0.036 0.074 0.044 0.034 0.052 0.088 0.048 0.027 0.058 0.046 0.062 0.048 0.042 0.127 0.045 0.151 0.094 0.089 0.049 0.074 0.039 0.096 0.080 0.056 0.012
interior_color_x4 -0.050 -0.022 -0.046 -0.049 0.014 -0.022 -0.065 0.085 -0.007 -0.003 0.028 -0.043 0.093 -0.013 -0.032 0.071 -0.073 -0.003 -0.105 -0.007 -0.027 0.024 -0.030 -0.014 0.058 -0.048 -0.079 0.256 0.007 -0.894 1.000 0.198 0.126 0.146 0.187 0.102 0.409 0.175 0.285 0.223 0.123 0.169 0.165 0.214 0.148 0.146 0.047 0.156 0.125 0.138 0.497 0.048 0.112 0.152 0.076 0.183 0.131 -0.022 0.070 0.075 0.037 0.027 0.105 0.151 0.189 0.052 0.126 0.067 0.103 0.091 0.076 0.145 0.105 0.105 0.056 0.041 0.050 0.041 0.119 0.063 0.117 0.121 0.110 0.054 0.095 0.053 0.054 0.047 0.048 0.087 0.067 0.037 0.036 0.083 0.047 0.040 0.100 0.045 0.043 0.056 0.056 0.151 0.042 0.133 0.112 0.053 0.028 0.079 0.037 0.055 -0.029 0.087 -0.016
make_Acura 0.012 0.010 0.020 0.070 0.000 0.006 0.043 0.011 0.013 0.005 0.018 0.174 0.122 0.101 0.000 0.010 0.029 0.069 0.097 0.085 0.067 0.105 0.026 0.005 0.034 0.019 0.113 0.195 0.270 0.279 0.198 1.000 0.026 0.032 0.014 0.025 0.047 0.024 0.045 0.021 0.026 0.037 0.017 0.042 0.025 0.014 0.019 0.019 0.026 0.037 0.038 0.008 0.017 0.030 0.026 0.035 0.012 0.045 0.024 0.013 0.016 0.008 0.028 0.029 0.023 0.010 0.018 0.020 0.019 0.022 0.015 0.028 0.007 0.022 0.395 0.230 0.015 0.013 0.596 0.021 0.028 0.020 0.022 0.247 0.019 0.020 0.012 0.011 0.007 0.023 0.012 0.016 0.011 0.022 0.008 0.011 0.015 0.007 0.016 0.016 0.012 0.032 0.012 0.016 0.016 0.023 0.055 0.025 0.016 0.021 -0.025 0.048 -0.032
make_Audi 0.017 0.043 0.044 0.134 0.000 0.009 0.056 0.063 0.052 0.068 0.000 0.233 0.218 0.168 0.095 0.084 0.034 0.056 0.087 0.047 0.062 0.077 0.016 0.000 0.027 0.020 0.093 0.124 0.131 0.131 0.126 0.026 1.000 0.042 0.019 0.033 0.061 0.031 0.059 0.027 0.035 0.048 0.022 0.054 0.032 0.020 0.026 0.025 0.034 0.049 0.049 0.011 0.022 0.039 0.034 0.046 0.016 0.075 0.070 0.018 0.021 0.163 0.037 0.029 0.167 0.014 0.015 0.016 0.025 0.028 0.046 0.037 0.020 0.376 0.019 0.022 0.018 0.018 0.038 0.009 0.036 0.021 0.021 0.021 0.026 0.014 0.047 0.011 0.274 0.031 0.016 0.044 0.025 0.014 0.012 0.015 0.095 0.010 0.021 0.022 0.016 0.034 0.067 0.022 0.166 0.094 0.016 0.045 0.021 0.028 0.098 0.079 -0.074
make_BMW 0.022 0.137 0.096 0.013 0.000 0.013 0.069 0.088 0.097 0.009 0.010 0.308 0.199 0.228 0.072 0.109 0.060 0.133 0.138 0.101 0.085 0.073 0.027 0.012 0.007 0.009 0.081 0.138 0.148 0.150 0.146 0.032 0.042 1.000 0.024 0.041 0.075 0.038 0.072 0.034 0.043 0.059 0.028 0.067 0.040 0.025 0.032 0.031 0.042 0.060 0.060 0.015 0.028 0.048 0.043 0.056 0.021 0.089 0.029 0.015 0.027 0.037 0.046 0.044 0.032 0.027 0.026 0.222 0.031 0.051 0.014 0.046 0.025 0.015 0.028 0.027 0.024 0.022 0.015 0.021 0.185 0.026 0.012 0.060 0.045 0.012 0.023 0.119 0.023 0.025 0.107 0.085 0.068 0.023 0.008 0.018 0.026 0.113 0.021 0.020 0.020 0.014 0.012 0.027 0.029 0.032 0.016 0.040 0.126 0.027 0.125 0.104 -0.050
make_Buick 0.009 0.011 0.014 0.009 0.007 0.001 0.033 0.051 0.027 0.000 0.000 0.148 0.098 0.085 0.080 0.096 0.020 0.090 0.089 0.082 0.224 0.098 0.020 0.000 0.029 0.020 0.050 0.222 0.263 0.205 0.187 0.014 0.019 0.024 1.000 0.019 0.036 0.018 0.035 0.015 0.020 0.028 0.012 0.032 0.019 0.010 0.014 0.014 0.019 0.029 0.029 0.004 0.012 0.023 0.020 0.027 0.008 0.024 0.016 0.009 0.011 0.202 0.022 0.125 0.017 0.006 0.013 0.015 0.156 0.044 0.010 0.021 0.011 0.017 0.016 0.010 0.011 0.009 0.022 0.016 0.403 0.015 0.016 0.008 0.014 0.015 0.012 0.007 0.002 0.015 0.011 0.012 0.007 0.017 0.004 0.007 0.011 0.002 0.012 0.013 0.008 0.116 0.008 0.012 0.011 0.017 0.008 0.019 0.012 0.016 -0.085 0.036 -0.049
make_Cadillac 0.017 0.014 0.022 0.017 0.000 0.009 0.055 0.058 0.002 0.008 0.014 0.163 0.147 0.116 0.033 0.048 0.023 0.329 0.083 0.109 0.092 0.332 0.122 0.015 0.064 0.034 0.151 0.158 0.106 0.091 0.102 0.025 0.033 0.041 0.019 1.000 0.060 0.030 0.057 0.027 0.034 0.047 0.022 0.053 0.032 0.019 0.025 0.024 0.033 0.047 0.048 0.011 0.022 0.038 0.034 0.045 0.016 0.001 0.031 0.017 0.021 0.031 0.036 0.037 0.030 0.014 0.024 0.025 0.024 0.028 0.016 0.036 0.066 0.027 0.020 0.021 0.020 0.016 0.037 0.195 0.035 0.026 0.023 0.016 0.024 0.025 0.022 0.015 0.117 0.024 0.033 0.027 0.010 0.028 0.325 0.015 0.020 0.010 0.021 0.017 0.016 0.041 0.007 0.019 0.139 0.231 0.016 0.031 0.408 0.027 0.082 0.004 0.002
make_Chevrolet 0.021 0.014 0.019 0.022 0.007 0.020 0.192 0.070 0.039 0.018 0.016 0.164 0.163 0.275 0.218 0.218 0.030 0.213 0.135 0.170 0.094 0.137 0.016 0.000 0.032 0.063 0.426 0.450 0.364 0.321 0.409 0.047 0.061 0.075 0.036 0.060 1.000 0.056 0.105 0.050 0.063 0.087 0.041 0.097 0.059 0.036 0.047 0.046 0.061 0.087 0.088 0.023 0.042 0.071 0.062 0.082 0.031 0.017 0.052 0.020 0.040 0.028 0.067 0.380 0.054 0.027 0.044 0.039 0.066 0.196 0.037 0.065 0.037 0.050 0.100 0.028 0.039 0.033 0.062 0.089 0.066 0.048 0.052 0.031 0.313 0.048 0.029 0.029 0.015 0.302 0.039 0.024 0.029 0.136 0.023 0.075 0.038 0.021 0.035 0.039 0.216 0.305 0.030 0.040 0.039 0.054 0.031 0.058 0.040 0.051 -0.148 0.060 -0.049
make_Dodge 0.016 0.013 0.170 0.016 0.000 0.025 0.046 0.009 0.018 0.007 0.000 0.459 0.123 0.312 0.041 0.055 0.197 0.159 0.060 0.177 0.128 0.146 0.032 0.008 0.000 0.030 0.092 0.157 0.159 0.162 0.175 0.024 0.031 0.038 0.018 0.030 0.056 1.000 0.054 0.025 0.032 0.044 0.020 0.050 0.030 0.018 0.024 0.023 0.031 0.045 0.045 0.010 0.021 0.036 0.032 0.042 0.015 0.007 0.028 0.016 0.017 0.013 0.551 0.030 0.153 0.013 0.022 0.024 0.023 0.026 0.018 0.025 0.018 0.013 0.026 0.011 0.019 0.016 0.279 0.025 0.013 0.024 0.027 0.015 0.023 0.024 0.021 0.014 0.009 0.028 0.015 0.020 0.014 0.027 0.010 0.014 0.019 0.009 0.020 0.020 0.015 0.034 0.015 0.020 0.019 0.027 0.015 0.214 0.020 0.026 0.048 0.033 -0.004
make_Ford 0.047 0.000 0.015 0.023 0.000 0.017 0.120 0.011 0.114 0.011 0.004 0.207 0.098 0.168 0.018 0.039 0.037 0.121 0.123 0.122 0.080 0.135 0.046 0.080 0.058 0.009 0.149 0.300 0.404 0.453 0.285 0.045 0.059 0.072 0.035 0.057 0.105 0.054 1.000 0.048 0.060 0.083 0.039 0.093 0.056 0.035 0.045 0.044 0.059 0.084 0.085 0.022 0.040 0.068 0.060 0.079 0.030 -0.012 0.200 0.029 0.038 0.026 0.050 0.171 0.044 0.026 0.026 0.098 0.288 0.045 0.036 0.061 0.035 0.051 0.033 0.052 0.037 0.030 0.048 0.044 0.358 0.041 0.309 0.030 0.033 0.046 0.039 0.028 0.021 0.053 0.030 0.038 0.021 0.024 0.000 0.028 0.028 0.020 0.038 0.039 0.029 0.055 0.022 0.055 0.038 0.052 0.030 0.133 0.038 0.046 0.011 0.028 -0.022
make_GMC 0.013 0.011 0.025 0.014 0.000 0.007 0.215 0.053 0.067 0.005 0.004 0.210 0.185 0.268 0.021 0.010 0.022 0.090 0.082 0.070 0.139 0.112 0.000 0.006 0.022 0.029 0.246 0.251 0.201 0.181 0.223 0.021 0.027 0.034 0.015 0.027 0.050 0.025 0.048 1.000 0.028 0.039 0.018 0.044 0.026 0.016 0.021 0.020 0.027 0.040 0.040 0.009 0.018 0.032 0.028 0.037 0.013 0.031 0.317 0.014 0.017 0.011 0.030 0.031 0.025 0.011 0.019 0.021 0.020 0.023 0.016 0.030 0.082 0.024 0.023 0.018 0.017 0.014 0.031 0.144 0.030 0.014 0.024 0.054 0.021 0.044 0.018 0.042 0.008 0.025 0.017 0.017 0.012 0.024 0.009 0.012 0.017 0.063 0.017 0.099 0.013 0.034 0.013 0.018 0.428 0.018 0.013 0.023 0.017 0.023 0.014 0.044 -0.036
make_Honda 0.018 0.014 0.022 0.015 0.082 0.094 0.041 0.007 0.036 0.009 0.003 0.151 0.224 0.092 0.055 0.079 0.039 0.102 0.163 0.144 0.105 0.111 0.036 0.009 0.000 0.042 0.093 0.115 0.133 0.133 0.123 0.026 0.035 0.043 0.020 0.034 0.063 0.032 0.060 0.028 1.000 0.050 0.023 0.055 0.033 0.020 0.027 0.026 0.035 0.050 0.050 0.012 0.023 0.040 0.035 0.047 0.017 0.050 0.032 0.019 0.022 0.015 0.038 0.037 0.031 0.015 0.012 0.027 0.026 0.029 0.017 0.038 0.019 0.030 0.064 0.023 0.021 0.018 0.039 0.121 0.034 0.027 0.030 0.017 0.026 0.027 0.373 0.016 0.011 0.379 0.022 0.022 0.016 0.049 0.012 0.016 0.021 0.052 0.022 0.292 0.117 0.042 0.302 0.023 0.022 0.031 0.017 0.033 0.022 0.029 -0.099 0.072 -0.057
make_Hyundai 0.025 0.021 0.035 0.026 0.004 0.015 0.032 0.034 0.024 0.014 0.012 0.203 0.194 0.112 0.076 0.104 0.043 0.133 0.106 0.065 0.121 0.055 0.000 0.014 0.019 0.029 0.183 0.180 0.288 0.291 0.169 0.037 0.048 0.059 0.028 0.047 0.087 0.044 0.083 0.039 0.050 1.000 0.032 0.077 0.046 0.029 0.037 0.036 0.048 0.069 0.070 0.018 0.033 0.056 0.049 0.065 0.024 -0.079 0.045 0.026 0.031 0.039 0.161 0.053 0.043 0.016 0.035 0.035 0.070 0.041 0.029 0.052 0.029 0.042 0.039 0.032 0.292 0.042 0.378 0.040 0.052 0.118 0.042 0.024 0.138 0.037 0.300 0.016 0.017 0.041 0.194 0.032 0.023 0.042 0.018 0.023 0.030 0.008 0.031 0.126 0.024 0.035 0.024 0.028 0.031 0.043 0.018 0.046 0.031 0.168 -0.144 0.112 0.103
make_INFINITI 0.010 0.008 0.000 0.010 0.000 0.004 0.037 0.031 0.000 0.002 0.000 0.141 0.145 0.088 0.056 0.054 0.011 0.054 0.088 0.074 0.075 0.091 0.023 0.003 0.033 0.021 0.170 0.169 0.242 0.272 0.165 0.017 0.022 0.028 0.012 0.022 0.041 0.020 0.039 0.018 0.023 0.032 1.000 0.036 0.021 0.012 0.017 0.016 0.022 0.032 0.033 0.006 0.014 0.026 0.023 0.030 0.010 0.017 0.021 0.011 0.013 0.005 0.024 0.025 0.020 0.019 0.077 0.012 0.016 0.018 0.012 0.024 0.012 0.019 0.079 0.012 0.013 0.011 0.025 0.018 0.023 0.070 0.019 0.010 0.040 0.017 0.014 0.009 0.005 0.019 0.013 0.018 0.212 0.331 0.006 0.024 0.087 0.004 0.013 0.052 0.009 0.028 0.007 0.010 0.007 0.158 0.010 0.021 0.013 0.018 0.029 0.012 -0.024
make_Jeep 0.028 0.019 0.049 0.029 0.005 0.017 0.032 0.164 0.130 0.016 0.014 0.454 0.139 0.201 0.134 0.116 0.054 0.264 0.298 0.306 0.486 0.269 0.055 0.003 0.042 0.005 0.164 0.208 0.332 0.184 0.214 0.042 0.054 0.067 0.032 0.053 0.097 0.050 0.093 0.044 0.055 0.077 0.036 1.000 0.052 0.032 0.042 0.040 0.054 0.077 0.078 0.020 0.037 0.062 0.055 0.073 0.027 -0.000 0.050 0.030 0.086 0.025 0.367 0.060 0.049 0.168 0.039 0.038 0.041 0.083 0.033 0.447 0.021 0.046 0.079 0.036 0.029 0.091 0.060 0.045 0.058 0.043 0.047 0.027 0.041 0.042 0.037 0.026 0.019 0.154 0.035 0.036 0.025 0.048 0.020 0.103 0.034 0.019 0.051 0.030 0.009 0.040 0.028 0.036 0.035 0.048 0.027 0.052 0.035 0.042 0.135 0.052 0.031
make_Kia 0.016 0.013 0.029 0.015 0.037 0.023 0.054 0.044 0.004 0.008 0.006 0.116 0.133 0.070 0.052 0.073 0.033 0.177 0.187 0.134 0.183 0.095 0.072 0.008 0.060 0.015 0.086 0.130 0.135 0.151 0.148 0.025 0.032 0.040 0.019 0.032 0.059 0.030 0.056 0.026 0.033 0.046 0.021 0.052 1.000 0.019 0.025 0.024 0.032 0.047 0.047 0.011 0.022 0.038 0.033 0.044 0.016 -0.015 0.061 0.017 0.021 0.010 0.036 0.032 0.029 0.013 0.030 0.258 0.024 0.021 0.019 0.036 0.009 0.028 0.020 0.342 0.020 0.017 0.036 0.027 0.020 0.025 0.028 0.016 0.025 0.025 0.022 0.062 0.010 0.029 0.036 0.021 0.033 0.350 0.011 0.015 0.020 0.222 0.185 0.021 0.014 0.032 0.016 0.021 0.020 0.029 0.016 0.031 0.021 0.027 -0.077 0.061 0.054
make_Land Rover 0.009 0.006 0.018 0.009 0.000 0.001 0.033 0.074 0.049 0.000 0.001 0.178 0.117 0.099 0.062 0.055 0.022 0.073 0.088 0.088 0.038 0.058 0.020 0.000 0.031 0.020 0.040 0.149 0.229 0.203 0.146 0.014 0.020 0.025 0.010 0.019 0.036 0.018 0.035 0.016 0.020 0.029 0.012 0.032 0.019 1.000 0.014 0.014 0.020 0.029 0.029 0.004 0.012 0.023 0.020 0.027 0.008 0.034 0.018 0.009 0.012 0.007 0.022 0.022 0.017 0.006 0.013 0.015 0.014 0.016 0.011 0.022 0.102 0.017 0.016 0.012 0.011 0.165 0.022 0.016 0.254 0.015 0.017 0.010 0.014 0.015 0.012 0.165 0.003 0.018 0.011 0.208 0.007 0.017 0.004 0.075 0.011 0.002 0.012 0.012 0.008 0.051 0.008 0.012 0.011 0.131 0.008 0.019 0.012 0.016 0.066 0.032 -0.025
make_Lexus 0.013 0.010 0.019 0.013 0.000 0.006 0.043 0.014 0.058 0.005 0.003 0.157 0.161 0.187 0.017 0.013 0.009 0.091 0.113 0.080 0.061 0.190 0.024 0.005 0.010 0.044 0.054 0.086 0.124 0.074 0.047 0.019 0.026 0.032 0.014 0.025 0.047 0.024 0.045 0.021 0.027 0.037 0.017 0.042 0.025 0.014 1.000 0.019 0.026 0.037 0.038 0.008 0.017 0.030 0.026 0.035 0.012 0.030 0.013 0.009 0.016 0.022 0.028 0.029 0.022 0.255 0.174 0.017 0.019 0.022 0.021 0.017 0.024 0.022 0.022 0.081 0.018 0.013 0.027 0.027 0.027 0.197 0.016 0.045 0.000 0.038 0.011 0.109 0.007 0.023 0.045 0.036 0.011 0.022 0.008 0.056 0.015 0.007 0.033 0.078 0.012 0.032 0.035 0.015 0.019 0.017 0.078 0.025 0.016 0.018 0.038 0.068 -0.067
make_Lincoln 0.012 0.009 0.022 0.012 0.000 0.005 0.042 0.074 0.035 0.004 0.002 0.164 0.129 0.106 0.042 0.038 0.013 0.059 0.068 0.140 0.044 0.065 0.026 0.000 0.000 0.025 0.102 0.125 0.119 0.156 0.156 0.019 0.025 0.031 0.014 0.024 0.046 0.023 0.044 0.020 0.026 0.036 0.016 0.040 0.024 0.014 0.019 1.000 0.025 0.036 0.036 0.007 0.016 0.029 0.025 0.034 0.011 -0.012 0.023 0.409 0.015 0.010 0.027 0.028 0.022 0.009 0.018 0.019 0.024 0.021 0.326 0.027 0.014 0.021 0.021 0.016 0.015 0.012 0.013 0.122 0.027 0.018 0.021 0.314 0.019 0.019 0.016 0.100 0.006 0.022 0.015 0.016 0.010 0.022 0.007 0.010 0.015 0.006 0.012 0.016 0.075 0.031 0.011 0.016 0.015 0.021 0.276 0.024 0.015 0.019 0.093 0.044 0.016
make_Mazda 0.017 0.030 0.027 0.016 0.000 0.000 0.056 0.083 0.053 0.008 0.007 0.211 0.199 0.098 0.055 0.051 0.015 0.130 0.149 0.170 0.157 0.127 0.035 0.009 0.013 0.059 0.100 0.086 0.101 0.116 0.125 0.026 0.034 0.042 0.019 0.033 0.061 0.031 0.059 0.027 0.035 0.048 0.022 0.054 0.032 0.020 0.026 0.025 1.000 0.049 0.049 0.012 0.023 0.039 0.034 0.046 0.016 -0.070 0.031 0.018 0.021 0.015 0.037 0.038 0.030 0.014 0.008 0.026 0.025 0.217 0.020 0.037 0.020 0.029 0.029 0.022 0.021 0.018 0.038 0.028 0.032 0.022 0.037 0.016 0.026 0.026 0.023 0.015 0.011 0.031 0.021 0.022 0.015 0.046 0.012 0.139 0.590 0.010 0.022 0.020 0.016 0.042 0.017 0.022 0.021 0.400 0.016 0.032 0.022 0.185 -0.095 0.091 0.093
make_Mercedes-Benz 0.218 0.035 0.012 0.000 0.004 0.096 0.077 0.112 0.109 0.001 0.000 0.402 0.320 0.181 0.064 0.122 0.106 0.101 0.143 0.170 0.151 0.135 0.053 0.000 0.014 0.031 0.099 0.153 0.191 0.115 0.138 0.037 0.049 0.060 0.029 0.047 0.087 0.045 0.084 0.040 0.050 0.069 0.032 0.077 0.047 0.029 0.037 0.036 0.049 1.000 0.070 0.018 0.033 0.056 0.050 0.066 0.025 0.021 0.041 0.087 0.103 0.014 0.044 0.043 0.040 0.020 0.035 0.029 0.037 0.077 0.295 0.065 0.065 0.026 0.049 0.068 0.032 0.068 0.047 0.025 0.052 0.012 0.021 0.024 0.035 0.040 0.033 0.053 0.023 0.044 0.083 0.115 0.225 0.037 0.018 0.022 0.030 0.015 0.032 0.032 0.024 0.047 0.025 0.082 0.074 0.033 0.111 0.189 0.058 0.038 0.285 0.027 -0.010
make_Nissan 0.012 0.019 0.016 0.020 0.004 0.015 0.021 0.070 0.132 0.000 0.001 0.243 0.239 0.196 0.150 0.178 0.032 0.108 0.122 0.115 0.068 0.153 0.000 0.014 0.037 0.051 0.091 0.499 0.355 0.502 0.497 0.038 0.049 0.060 0.029 0.048 0.088 0.045 0.085 0.040 0.050 0.070 0.033 0.078 0.047 0.029 0.038 0.036 0.049 0.070 1.000 0.018 0.033 0.057 0.050 0.066 0.025 -0.085 0.046 0.092 0.015 0.022 0.054 0.035 0.416 0.020 0.258 0.038 0.037 0.041 0.030 0.054 0.222 0.043 0.042 0.028 0.041 0.026 0.055 0.079 0.053 0.258 0.042 0.025 0.035 0.038 0.033 0.023 0.017 0.045 0.031 0.032 0.023 0.043 0.018 0.023 0.023 0.017 0.195 0.032 0.024 0.061 0.025 0.268 0.031 0.094 0.025 0.047 0.032 0.041 -0.143 0.043 0.038
make_Porsche 0.002 0.000 0.053 0.002 0.000 0.000 0.021 0.023 0.030 0.000 0.000 0.085 0.132 0.073 0.021 0.035 0.023 0.054 0.039 0.038 0.043 0.052 0.007 0.000 0.000 0.007 0.033 0.048 0.047 0.045 0.048 0.008 0.011 0.015 0.004 0.011 0.023 0.010 0.022 0.009 0.012 0.018 0.006 0.020 0.011 0.004 0.008 0.007 0.012 0.018 0.018 1.000 0.006 0.014 0.012 0.017 0.001 0.053 0.346 0.040 0.005 0.000 0.013 0.013 0.014 0.000 0.007 0.008 0.007 0.009 0.004 0.013 0.004 0.009 0.009 0.009 0.005 0.004 0.009 0.009 0.013 0.008 0.009 0.000 0.008 0.008 0.006 0.000 0.000 0.010 0.005 0.005 0.000 0.010 0.000 0.000 0.005 0.000 0.400 0.005 0.000 0.013 0.117 0.004 0.002 0.010 0.000 0.011 0.005 0.009 0.006 0.082 -0.050
make_RAM 0.067 0.008 0.020 0.011 0.000 0.004 0.383 0.187 0.056 0.003 0.022 0.400 0.318 0.268 0.045 0.036 0.024 0.177 0.148 0.170 0.189 0.143 0.023 0.003 0.035 0.024 0.087 0.098 0.130 0.141 0.112 0.017 0.022 0.028 0.012 0.022 0.042 0.021 0.040 0.018 0.023 0.033 0.014 0.037 0.022 0.012 0.017 0.016 0.023 0.033 0.033 0.006 1.000 0.026 0.023 0.031 0.010 0.001 0.021 0.011 0.016 0.008 0.025 0.025 0.020 0.008 0.147 0.017 0.016 0.019 0.013 0.025 0.013 0.019 0.019 0.014 0.055 0.011 0.025 0.018 0.024 0.017 0.651 0.010 0.017 0.017 0.015 0.009 0.005 0.020 0.014 0.057 0.009 0.020 0.006 0.009 0.013 0.005 0.014 0.014 0.010 0.028 0.290 0.014 0.014 0.020 0.010 0.022 0.000 0.018 0.108 0.019 0.051
make_Subaru 0.020 0.017 0.034 0.074 0.000 0.012 0.065 0.070 0.030 0.000 0.006 0.142 0.114 0.157 0.119 0.105 0.043 0.149 0.174 0.122 0.109 0.210 0.036 0.011 0.057 0.040 0.129 0.119 0.201 0.203 0.152 0.030 0.039 0.048 0.023 0.038 0.071 0.036 0.068 0.032 0.040 0.056 0.026 0.062 0.038 0.023 0.030 0.029 0.039 0.056 0.057 0.014 0.026 1.000 0.040 0.053 0.019 -0.066 0.037 0.018 0.025 0.015 0.043 0.173 0.133 0.015 0.028 0.030 0.029 0.033 0.023 0.043 0.023 0.599 0.033 0.026 0.024 0.021 0.025 0.032 0.042 0.031 0.034 0.019 0.030 0.030 0.027 0.018 0.013 0.036 0.025 0.025 0.018 0.071 0.000 0.018 0.024 0.013 0.025 0.025 0.019 0.049 0.020 0.026 0.025 0.035 0.019 0.037 0.070 0.355 -0.054 0.040 0.060
make_Toyota 0.018 0.013 0.006 0.015 0.003 0.004 0.025 0.039 0.031 0.000 0.003 0.133 0.055 0.082 0.046 0.050 0.000 0.068 0.185 0.154 0.089 0.136 0.027 0.009 0.088 0.156 0.078 0.142 0.093 0.039 0.076 0.026 0.034 0.043 0.020 0.034 0.062 0.032 0.060 0.028 0.035 0.049 0.023 0.055 0.033 0.020 0.026 0.025 0.034 0.050 0.050 0.012 0.023 0.040 1.000 0.047 0.017 0.063 0.032 0.000 0.267 0.015 0.038 0.039 0.031 0.011 0.025 0.204 0.019 0.029 0.040 0.023 0.020 0.030 0.125 0.018 0.066 0.219 0.038 0.021 0.030 0.020 0.030 0.032 0.021 0.027 0.020 0.016 0.023 0.031 0.013 0.160 0.015 0.028 0.012 0.026 0.021 0.011 0.022 0.022 0.019 0.043 0.017 0.164 0.022 0.026 0.017 0.047 0.022 0.022 -0.058 0.093 -0.089
make_Volkswagen 0.024 0.016 0.040 0.022 0.003 0.014 0.076 0.026 0.039 0.074 0.077 0.224 0.256 0.208 0.011 0.035 0.043 0.199 0.303 0.163 0.137 0.118 0.031 0.013 0.059 0.048 0.102 0.142 0.096 0.105 0.183 0.035 0.046 0.056 0.027 0.045 0.082 0.042 0.079 0.037 0.047 0.065 0.030 0.073 0.044 0.027 0.035 0.034 0.046 0.066 0.066 0.017 0.031 0.053 0.047 1.000 0.023 -0.081 0.043 0.025 0.029 0.017 0.050 0.063 0.041 0.020 0.033 0.036 0.015 0.039 0.028 0.208 0.043 0.040 0.039 0.018 0.029 0.023 0.051 0.036 0.049 0.036 0.026 0.023 0.055 0.445 0.031 0.022 0.016 0.041 0.023 0.030 0.019 0.040 0.017 0.021 0.029 0.015 0.030 0.030 0.023 0.442 0.023 0.030 0.029 0.030 0.023 0.275 0.030 0.038 -0.114 0.094 0.087
make_Volvo 0.007 0.000 0.015 0.007 0.000 0.000 0.028 0.002 0.032 0.076 0.000 0.108 0.107 0.143 0.036 0.029 0.019 0.041 0.035 0.028 0.030 0.043 0.000 0.000 0.054 0.083 0.063 0.204 0.129 0.152 0.131 0.012 0.016 0.021 0.008 0.016 0.031 0.015 0.030 0.013 0.017 0.024 0.010 0.027 0.016 0.008 0.012 0.011 0.016 0.025 0.025 0.001 0.010 0.019 0.017 0.023 1.000 0.008 0.015 0.007 0.022 0.000 0.054 0.019 0.014 0.013 0.011 0.012 0.012 0.022 0.006 0.014 0.008 0.014 0.014 0.010 0.264 0.007 0.019 0.013 0.018 0.012 0.014 0.006 0.012 0.200 0.010 0.005 0.000 0.015 0.223 0.009 0.007 0.012 0.001 0.005 0.009 0.000 0.041 0.009 0.006 0.095 0.006 0.010 0.009 0.014 0.214 0.016 0.009 0.013 0.031 0.030 -0.017
mileage -0.021 0.040 0.087 0.006 -0.026 -0.007 -0.014 -0.143 0.138 0.060 0.013 0.015 0.005 0.063 -0.103 0.074 0.072 -0.031 0.009 0.025 0.032 0.014 -0.108 0.082 0.108 -0.072 -0.055 0.055 0.047 0.028 -0.022 0.045 0.075 0.089 0.024 0.001 0.017 0.007 -0.012 0.031 0.050 -0.079 0.017 -0.000 -0.015 0.034 0.030 -0.012 -0.070 0.021 -0.085 0.053 0.001 -0.066 0.063 -0.081 0.008 1.000 0.013 0.016 0.019 0.000 0.000 0.000 0.000 0.010 0.013 0.000 0.000 0.000 0.000 0.023 0.020 0.000 0.019 0.000 0.000 0.011 0.010 0.020 0.000 0.008 0.017 0.000 0.012 0.000 0.009 0.000 0.000 0.018 0.006 0.027 0.078 0.000 0.008 0.005 0.015 0.005 0.000 0.051 0.014 0.032 0.023 0.000 0.016 0.007 0.013 0.000 0.013 0.015 -0.263 0.282 -0.859
model_hashed_0 0.016 0.013 0.055 0.047 0.000 0.008 0.393 0.194 0.069 0.009 0.000 0.304 0.238 0.180 0.078 0.076 0.020 0.050 0.035 0.034 0.043 0.043 0.032 0.019 0.026 0.011 0.045 0.074 0.048 0.076 0.070 0.024 0.070 0.029 0.016 0.031 0.052 0.028 0.200 0.317 0.032 0.045 0.021 0.050 0.061 0.018 0.013 0.023 0.031 0.041 0.046 0.346 0.021 0.037 0.032 0.043 0.015 0.013 1.000 0.011 0.013 0.008 0.024 0.024 0.019 0.008 0.015 0.016 0.016 0.018 0.012 0.024 0.012 0.019 0.018 0.013 0.013 0.010 0.024 0.018 0.023 0.017 0.018 0.009 0.016 0.016 0.014 0.009 0.004 0.019 0.013 0.013 0.008 0.019 0.011 0.008 0.013 0.004 0.013 0.013 0.009 0.027 0.009 0.013 0.013 0.019 0.009 0.021 0.013 0.018 0.097 0.086 -0.004
model_hashed_1 0.027 0.006 0.016 0.031 0.000 0.264 0.031 0.082 0.051 0.027 0.000 0.132 0.074 0.049 0.092 0.044 0.109 0.045 0.048 0.062 0.031 0.038 0.059 0.000 0.068 0.041 0.035 0.055 0.068 0.074 0.075 0.013 0.018 0.015 0.009 0.017 0.020 0.016 0.029 0.014 0.019 0.026 0.011 0.030 0.017 0.009 0.009 0.409 0.018 0.087 0.092 0.040 0.011 0.018 0.000 0.025 0.007 0.016 0.011 1.000 0.005 0.000 0.013 0.013 0.010 0.000 0.007 0.008 0.007 0.009 0.004 0.013 0.004 0.010 0.009 0.006 0.005 0.002 0.013 0.009 0.013 0.008 0.009 0.000 0.008 0.008 0.006 0.000 0.000 0.010 0.005 0.005 0.000 0.010 0.002 0.000 0.005 0.000 0.005 0.005 0.000 0.015 0.000 0.006 0.005 0.010 0.000 0.011 0.005 0.009 0.035 0.035 0.031
model_hashed_10 0.005 0.000 0.012 0.010 0.000 0.003 0.070 0.056 0.036 0.043 0.036 0.163 0.088 0.175 0.046 0.042 0.017 0.030 0.035 0.045 0.067 0.037 0.022 0.014 0.047 0.087 0.047 0.066 0.053 0.041 0.037 0.016 0.021 0.027 0.011 0.021 0.040 0.017 0.038 0.017 0.022 0.031 0.013 0.086 0.021 0.012 0.016 0.015 0.021 0.103 0.015 0.005 0.016 0.025 0.267 0.029 0.022 0.019 0.013 0.005 1.000 0.002 0.016 0.016 0.012 0.001 0.009 0.010 0.010 0.011 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.016 0.010 0.012 0.004 0.010 0.010 0.008 0.003 0.000 0.013 0.007 0.008 0.003 0.012 0.005 0.003 0.007 0.000 0.008 0.008 0.004 0.018 0.004 0.008 0.007 0.012 0.004 0.013 0.008 0.011 0.033 0.054 0.043
model_hashed_11 0.005 0.129 0.055 0.005 0.000 0.000 0.024 0.042 0.033 0.067 0.000 0.079 0.141 0.096 0.034 0.026 0.026 0.023 0.021 0.016 0.022 0.018 0.023 0.000 0.045 0.063 0.025 0.036 0.047 0.042 0.027 0.008 0.163 0.037 0.202 0.031 0.028 0.013 0.026 0.011 0.015 0.039 0.005 0.025 0.010 0.007 0.022 0.010 0.015 0.014 0.022 0.000 0.008 0.015 0.015 0.017 0.000 0.000 0.008 0.000 0.002 1.000 0.010 0.011 0.007 0.000 0.004 0.005 0.005 0.006 0.000 0.010 0.000 0.007 0.007 0.002 0.001 0.000 0.010 0.006 0.010 0.006 0.007 0.000 0.005 0.005 0.003 0.000 0.000 0.007 0.002 0.002 0.000 0.007 0.000 0.000 0.000 0.000 0.002 0.002 0.000 0.012 0.000 0.002 0.002 0.007 0.000 0.008 0.002 0.006 0.008 0.034 -0.011
model_hashed_12 0.031 0.016 0.033 0.019 0.000 0.011 0.061 0.123 0.083 0.010 0.004 0.334 0.091 0.269 0.078 0.078 0.010 0.120 0.104 0.135 0.144 0.120 0.038 0.028 0.048 0.039 0.063 0.100 0.161 0.094 0.105 0.028 0.037 0.046 0.022 0.036 0.067 0.551 0.050 0.030 0.038 0.161 0.024 0.367 0.036 0.022 0.028 0.027 0.037 0.044 0.054 0.013 0.025 0.043 0.038 0.050 0.054 0.000 0.024 0.013 0.016 0.010 1.000 0.029 0.023 0.010 0.018 0.020 0.019 0.021 0.015 0.028 0.015 0.022 0.022 0.016 0.015 0.013 0.029 0.021 0.028 0.020 0.022 0.012 0.019 0.020 0.017 0.011 0.007 0.023 0.016 0.016 0.011 0.022 0.013 0.011 0.015 0.007 0.016 0.016 0.012 0.032 0.012 0.016 0.016 0.023 0.012 0.025 0.016 0.021 0.016 0.056 -0.033
model_hashed_13 0.012 0.016 0.028 0.014 0.000 0.011 0.105 0.099 0.080 0.010 0.005 0.189 0.119 0.178 0.149 0.152 0.023 0.070 0.053 0.063 0.049 0.044 0.032 0.014 0.049 0.038 0.136 0.186 0.150 0.150 0.151 0.029 0.029 0.044 0.125 0.037 0.380 0.030 0.171 0.031 0.037 0.053 0.025 0.060 0.032 0.022 0.029 0.028 0.038 0.043 0.035 0.013 0.025 0.173 0.039 0.063 0.019 0.000 0.024 0.013 0.016 0.011 0.029 1.000 0.023 0.010 0.019 0.020 0.019 0.022 0.015 0.029 0.015 0.023 0.022 0.017 0.016 0.013 0.029 0.021 0.028 0.020 0.022 0.012 0.020 0.020 0.017 0.011 0.007 0.024 0.016 0.017 0.011 0.023 0.013 0.011 0.016 0.007 0.016 0.017 0.012 0.033 0.012 0.017 0.016 0.023 0.012 0.025 0.016 0.022 0.080 0.048 -0.018
model_hashed_14 0.008 0.017 0.022 0.017 0.000 0.008 0.044 0.052 0.050 0.007 0.007 0.131 0.112 0.080 0.044 0.060 0.131 0.042 0.049 0.051 0.041 0.062 0.031 0.000 0.041 0.025 0.059 0.161 0.142 0.211 0.189 0.023 0.167 0.032 0.017 0.030 0.054 0.153 0.044 0.025 0.031 0.043 0.020 0.049 0.029 0.017 0.022 0.022 0.030 0.040 0.416 0.014 0.020 0.133 0.031 0.041 0.014 0.000 0.019 0.010 0.012 0.007 0.023 0.023 1.000 0.007 0.014 0.016 0.015 0.017 0.011 0.023 0.011 0.018 0.017 0.013 0.012 0.010 0.023 0.017 0.023 0.016 0.018 0.009 0.015 0.016 0.013 0.008 0.004 0.019 0.012 0.013 0.008 0.018 0.010 0.008 0.012 0.003 0.013 0.013 0.009 0.026 0.009 0.013 0.012 0.018 0.009 0.020 0.013 0.017 -0.025 0.030 0.025
model_hashed_15 0.058 0.000 0.061 0.015 0.000 0.000 0.025 0.075 0.094 0.028 0.009 0.119 0.157 0.115 0.085 0.097 0.010 0.039 0.040 0.050 0.041 0.049 0.015 0.000 0.019 0.021 0.028 0.044 0.062 0.057 0.052 0.010 0.014 0.027 0.006 0.014 0.027 0.013 0.026 0.011 0.015 0.016 0.019 0.168 0.013 0.006 0.255 0.009 0.014 0.020 0.020 0.000 0.008 0.015 0.011 0.020 0.013 0.010 0.008 0.000 0.001 0.000 0.010 0.010 0.007 1.000 0.004 0.005 0.004 0.006 0.000 0.010 0.000 0.007 0.006 0.002 0.000 0.000 0.010 0.006 0.010 0.005 0.006 0.000 0.005 0.005 0.003 0.000 0.000 0.007 0.001 0.002 0.000 0.007 0.000 0.000 0.000 0.000 0.001 0.002 0.000 0.012 0.000 0.002 0.001 0.007 0.000 0.008 0.001 0.006 0.007 0.067 0.044
model_hashed_16 0.012 0.015 0.055 0.012 0.000 0.005 0.125 0.191 0.159 0.003 0.000 0.116 0.099 0.053 0.048 0.054 0.000 0.048 0.042 0.046 0.022 0.064 0.025 0.000 0.035 0.030 0.042 0.104 0.069 0.102 0.126 0.018 0.015 0.026 0.013 0.024 0.044 0.022 0.026 0.019 0.012 0.035 0.077 0.039 0.030 0.013 0.174 0.018 0.008 0.035 0.258 0.007 0.147 0.028 0.025 0.033 0.011 0.013 0.015 0.007 0.009 0.004 0.018 0.019 0.014 0.004 1.000 0.012 0.011 0.013 0.008 0.018 0.008 0.014 0.014 0.009 0.009 0.007 0.018 0.013 0.018 0.012 0.014 0.006 0.012 0.012 0.010 0.005 0.000 0.015 0.009 0.009 0.005 0.014 0.007 0.005 0.009 0.000 0.009 0.009 0.006 0.021 0.006 0.009 0.009 0.014 0.006 0.016 0.009 0.013 0.026 0.038 -0.022
model_hashed_17 0.028 0.049 0.157 0.009 0.000 0.006 0.039 0.097 0.045 0.000 0.000 0.185 0.063 0.162 0.039 0.056 0.149 0.043 0.094 0.057 0.048 0.040 0.025 0.005 0.078 0.121 0.040 0.066 0.060 0.069 0.067 0.020 0.016 0.222 0.015 0.025 0.039 0.024 0.098 0.021 0.027 0.035 0.012 0.038 0.258 0.015 0.017 0.019 0.026 0.029 0.038 0.008 0.017 0.030 0.204 0.036 0.012 0.000 0.016 0.008 0.010 0.005 0.020 0.020 0.016 0.005 0.012 1.000 0.013 0.014 0.009 0.020 0.009 0.015 0.015 0.010 0.010 0.008 0.020 0.014 0.019 0.013 0.015 0.007 0.013 0.013 0.011 0.006 0.000 0.016 0.010 0.010 0.006 0.015 0.008 0.006 0.010 0.000 0.010 0.010 0.007 0.022 0.007 0.010 0.010 0.015 0.007 0.017 0.010 0.014 0.011 0.052 -0.066
model_hashed_18 0.012 0.009 0.023 0.012 0.000 0.005 0.042 0.080 0.044 0.004 0.000 0.135 0.095 0.158 0.058 0.060 0.017 0.062 0.030 0.038 0.036 0.040 0.027 0.005 0.033 0.021 0.040 0.133 0.187 0.175 0.103 0.019 0.025 0.031 0.156 0.024 0.066 0.023 0.288 0.020 0.026 0.070 0.016 0.041 0.024 0.014 0.019 0.024 0.025 0.037 0.037 0.007 0.016 0.029 0.019 0.015 0.012 0.000 0.016 0.007 0.010 0.005 0.019 0.019 0.015 0.004 0.011 0.013 1.000 0.014 0.009 0.019 0.009 0.014 0.014 0.010 0.009 0.007 0.019 0.014 0.019 0.013 0.014 0.006 0.012 0.012 0.011 0.006 0.000 0.015 0.010 0.010 0.005 0.015 0.007 0.005 0.009 0.000 0.010 0.010 0.006 0.022 0.006 0.010 0.010 0.015 0.006 0.016 0.010 0.014 0.009 0.036 0.039
model_hashed_19 0.014 0.015 0.024 0.004 0.000 0.009 0.079 0.139 0.139 0.006 0.004 0.109 0.194 0.145 0.099 0.119 0.025 0.044 0.042 0.040 0.065 0.047 0.025 0.006 0.040 0.030 0.095 0.094 0.094 0.104 0.091 0.022 0.028 0.051 0.044 0.028 0.196 0.026 0.045 0.023 0.029 0.041 0.018 0.083 0.021 0.016 0.022 0.021 0.217 0.077 0.041 0.009 0.019 0.033 0.029 0.039 0.022 0.000 0.018 0.009 0.011 0.006 0.021 0.022 0.017 0.006 0.013 0.014 0.014 1.000 0.010 0.021 0.011 0.017 0.016 0.012 0.011 0.009 0.022 0.016 0.021 0.015 0.016 0.008 0.014 0.014 0.012 0.007 0.002 0.017 0.011 0.012 0.007 0.017 0.009 0.007 0.011 0.002 0.012 0.012 0.008 0.024 0.008 0.012 0.011 0.017 0.008 0.018 0.012 0.016 -0.038 0.000 -0.001
model_hashed_2 0.009 0.000 0.010 0.038 0.000 0.000 0.034 0.122 0.167 0.000 0.000 0.225 0.228 0.207 0.045 0.043 0.013 0.048 0.058 0.062 0.055 0.055 0.021 0.007 0.017 0.014 0.047 0.044 0.098 0.063 0.076 0.015 0.046 0.014 0.010 0.016 0.037 0.018 0.036 0.016 0.017 0.029 0.012 0.033 0.019 0.011 0.021 0.326 0.020 0.295 0.030 0.004 0.013 0.023 0.040 0.028 0.006 0.000 0.012 0.004 0.007 0.000 0.015 0.015 0.011 0.000 0.008 0.009 0.009 0.010 1.000 0.015 0.006 0.011 0.011 0.007 0.006 0.004 0.015 0.010 0.014 0.009 0.011 0.003 0.009 0.009 0.007 0.001 0.000 0.012 0.007 0.007 0.000 0.011 0.004 0.001 0.006 0.000 0.007 0.007 0.002 0.017 0.003 0.007 0.007 0.011 0.003 0.012 0.007 0.010 -0.016 0.010 -0.009
model_hashed_20 0.019 0.012 0.025 0.019 0.000 0.011 0.049 0.114 0.078 0.010 0.008 0.192 0.140 0.103 0.089 0.079 0.032 0.098 0.111 0.106 0.123 0.092 0.038 0.007 0.043 0.023 0.063 0.125 0.192 0.138 0.145 0.028 0.037 0.046 0.021 0.036 0.065 0.025 0.061 0.030 0.038 0.052 0.024 0.447 0.036 0.022 0.017 0.027 0.037 0.065 0.054 0.013 0.025 0.043 0.023 0.208 0.014 0.023 0.024 0.013 0.016 0.010 0.028 0.029 0.023 0.010 0.018 0.020 0.019 0.021 0.015 1.000 0.015 0.022 0.022 0.016 0.015 0.013 0.029 0.021 0.028 0.020 0.022 0.012 0.019 0.020 0.017 0.011 0.007 0.023 0.016 0.016 0.011 0.022 0.013 0.011 0.015 0.007 0.016 0.016 0.012 0.032 0.012 0.016 0.016 0.022 0.012 0.024 0.016 0.021 0.069 0.098 0.072
model_hashed_21 0.009 0.006 0.010 0.009 0.000 0.000 0.034 0.027 0.030 0.000 0.000 0.129 0.059 0.059 0.017 0.017 0.007 0.047 0.023 0.041 0.016 0.053 0.143 0.000 0.093 0.008 0.025 0.096 0.088 0.107 0.105 0.007 0.020 0.025 0.011 0.066 0.037 0.018 0.035 0.082 0.019 0.029 0.012 0.021 0.009 0.102 0.024 0.014 0.020 0.065 0.222 0.004 0.013 0.023 0.020 0.043 0.008 0.020 0.012 0.004 0.007 0.000 0.015 0.015 0.011 0.000 0.008 0.009 0.009 0.011 0.006 0.015 1.000 0.011 0.011 0.007 0.006 0.004 0.015 0.010 0.014 0.010 0.011 0.003 0.009 0.009 0.007 0.002 0.000 0.012 0.007 0.007 0.001 0.011 0.004 0.001 0.006 0.000 0.007 0.007 0.003 0.017 0.003 0.007 0.007 0.011 0.003 0.012 0.007 0.010 -0.030 0.000 -0.012
model_hashed_22 0.015 0.009 0.010 0.011 0.000 0.035 0.049 0.086 0.068 0.016 0.000 0.168 0.120 0.192 0.073 0.071 0.016 0.069 0.092 0.071 0.059 0.110 0.030 0.039 0.045 0.039 0.088 0.062 0.098 0.093 0.105 0.022 0.376 0.015 0.017 0.027 0.050 0.013 0.051 0.024 0.030 0.042 0.019 0.046 0.028 0.017 0.022 0.021 0.029 0.026 0.043 0.009 0.019 0.599 0.030 0.040 0.014 0.000 0.019 0.010 0.012 0.007 0.022 0.023 0.018 0.007 0.014 0.015 0.014 0.017 0.011 0.022 0.011 1.000 0.017 0.012 0.011 0.009 0.022 0.016 0.022 0.015 0.017 0.008 0.015 0.015 0.013 0.008 0.003 0.018 0.012 0.012 0.007 0.017 0.009 0.007 0.011 0.003 0.012 0.012 0.008 0.025 0.008 0.012 0.012 0.017 0.008 0.019 0.012 0.016 0.027 0.048 -0.050
model_hashed_23 0.067 0.011 0.018 0.019 0.139 0.166 0.048 0.121 0.102 0.003 0.047 0.163 0.208 0.126 0.075 0.084 0.027 0.036 0.043 0.049 0.050 0.039 0.197 0.006 0.124 0.026 0.050 0.047 0.072 0.075 0.056 0.395 0.019 0.028 0.016 0.020 0.100 0.026 0.033 0.023 0.064 0.039 0.079 0.079 0.020 0.016 0.022 0.021 0.029 0.049 0.042 0.009 0.019 0.033 0.125 0.039 0.014 0.019 0.018 0.009 0.012 0.007 0.022 0.022 0.017 0.006 0.014 0.015 0.014 0.016 0.011 0.022 0.011 0.017 1.000 0.012 0.011 0.009 0.022 0.016 0.021 0.015 0.017 0.008 0.014 0.015 0.013 0.007 0.003 0.018 0.012 0.012 0.007 0.017 0.009 0.007 0.011 0.002 0.012 0.012 0.008 0.025 0.008 0.012 0.012 0.017 0.008 0.019 0.012 0.016 -0.032 0.071 -0.064
model_hashed_24 0.010 0.008 0.056 0.007 0.000 0.003 0.032 0.083 0.089 0.001 0.000 0.177 0.141 0.129 0.000 0.013 0.019 0.071 0.038 0.039 0.054 0.035 0.114 0.030 0.073 0.018 0.030 0.048 0.050 0.055 0.041 0.230 0.022 0.027 0.010 0.021 0.028 0.011 0.052 0.018 0.023 0.032 0.012 0.036 0.342 0.012 0.081 0.016 0.022 0.068 0.028 0.009 0.014 0.026 0.018 0.018 0.010 0.000 0.013 0.006 0.008 0.002 0.016 0.017 0.013 0.002 0.009 0.010 0.010 0.012 0.007 0.016 0.007 0.012 0.012 1.000 0.007 0.005 0.017 0.011 0.016 0.011 0.012 0.004 0.010 0.010 0.009 0.004 0.000 0.013 0.008 0.008 0.003 0.012 0.006 0.003 0.007 0.000 0.008 0.008 0.004 0.019 0.004 0.008 0.008 0.012 0.004 0.014 0.008 0.012 -0.023 0.029 0.015
model_hashed_25 0.076 0.007 0.019 0.004 0.000 0.002 0.039 0.050 0.052 0.000 0.000 0.128 0.077 0.130 0.028 0.026 0.019 0.041 0.046 0.021 0.023 0.028 0.018 0.000 0.035 0.042 0.049 0.055 0.068 0.077 0.050 0.015 0.018 0.024 0.011 0.020 0.039 0.019 0.037 0.017 0.021 0.292 0.013 0.029 0.020 0.011 0.018 0.015 0.021 0.032 0.041 0.005 0.055 0.024 0.066 0.029 0.264 0.000 0.013 0.005 0.007 0.001 0.015 0.016 0.012 0.000 0.009 0.010 0.009 0.011 0.006 0.015 0.006 0.011 0.011 0.007 1.000 0.005 0.016 0.011 0.015 0.010 0.011 0.003 0.010 0.010 0.008 0.003 0.000 0.012 0.007 0.007 0.002 0.012 0.005 0.002 0.007 0.000 0.007 0.007 0.003 0.018 0.004 0.007 0.007 0.012 0.003 0.013 0.007 0.011 0.006 0.032 -0.031
model_hashed_26 0.007 0.016 0.007 0.003 0.000 0.000 0.030 0.065 0.057 0.000 0.000 0.105 0.078 0.080 0.015 0.012 0.010 0.021 0.029 0.041 0.052 0.029 0.018 0.000 0.039 0.053 0.035 0.053 0.044 0.043 0.041 0.013 0.018 0.022 0.009 0.016 0.033 0.016 0.030 0.014 0.018 0.042 0.011 0.091 0.017 0.165 0.013 0.012 0.018 0.068 0.026 0.004 0.011 0.021 0.219 0.023 0.007 0.011 0.010 0.002 0.005 0.000 0.013 0.013 0.010 0.000 0.007 0.008 0.007 0.009 0.004 0.013 0.004 0.009 0.009 0.005 0.005 1.000 0.013 0.009 0.013 0.008 0.009 0.000 0.007 0.008 0.006 0.000 0.000 0.010 0.005 0.005 0.000 0.009 0.002 0.000 0.004 0.000 0.005 0.005 0.000 0.015 0.000 0.005 0.005 0.009 0.000 0.011 0.005 0.009 -0.014 0.075 -0.065
model_hashed_27 0.019 0.006 0.010 0.020 0.000 0.011 0.056 0.103 0.066 0.010 0.000 0.201 0.114 0.131 0.059 0.046 0.037 0.104 0.079 0.076 0.069 0.064 0.037 0.061 0.029 0.037 0.086 0.119 0.163 0.170 0.119 0.596 0.038 0.015 0.022 0.037 0.062 0.279 0.048 0.031 0.039 0.378 0.025 0.060 0.036 0.022 0.027 0.013 0.038 0.047 0.055 0.009 0.025 0.025 0.038 0.051 0.019 0.010 0.024 0.013 0.016 0.010 0.029 0.029 0.023 0.010 0.018 0.020 0.019 0.022 0.015 0.029 0.015 0.022 0.022 0.017 0.016 0.013 1.000 0.021 0.028 0.020 0.022 0.012 0.020 0.020 0.017 0.011 0.007 0.024 0.016 0.016 0.011 0.023 0.013 0.011 0.016 0.007 0.016 0.016 0.012 0.033 0.012 0.017 0.016 0.023 0.012 0.025 0.016 0.021 -0.031 0.072 0.063
model_hashed_28 0.014 0.011 0.021 0.011 0.000 0.007 0.193 0.141 0.049 0.005 0.004 0.124 0.139 0.121 0.027 0.021 0.026 0.059 0.038 0.024 0.054 0.069 0.021 0.023 0.016 0.021 0.069 0.059 0.056 0.057 0.063 0.021 0.009 0.021 0.016 0.195 0.089 0.025 0.044 0.144 0.121 0.040 0.018 0.045 0.027 0.016 0.027 0.122 0.028 0.025 0.079 0.009 0.018 0.032 0.021 0.036 0.013 0.020 0.018 0.009 0.011 0.006 0.021 0.021 0.017 0.006 0.013 0.014 0.014 0.016 0.010 0.021 0.010 0.016 0.016 0.011 0.011 0.009 0.021 1.000 0.021 0.014 0.016 0.008 0.014 0.014 0.012 0.007 0.002 0.017 0.011 0.011 0.007 0.016 0.009 0.007 0.011 0.001 0.011 0.011 0.007 0.024 0.008 0.011 0.011 0.016 0.008 0.018 0.011 0.015 0.008 0.026 0.015
model_hashed_29 0.019 0.014 0.022 0.019 0.000 0.010 0.060 0.117 0.073 0.009 0.008 0.145 0.121 0.117 0.031 0.050 0.033 0.062 0.052 0.055 0.087 0.065 0.033 0.004 0.016 0.053 0.059 0.126 0.191 0.191 0.117 0.028 0.036 0.185 0.403 0.035 0.066 0.013 0.358 0.030 0.034 0.052 0.023 0.058 0.020 0.254 0.027 0.027 0.032 0.052 0.053 0.013 0.024 0.042 0.030 0.049 0.018 0.000 0.023 0.013 0.016 0.010 0.028 0.028 0.023 0.010 0.018 0.019 0.019 0.021 0.014 0.028 0.014 0.022 0.021 0.016 0.015 0.013 0.028 0.021 1.000 0.020 0.022 0.011 0.019 0.019 0.017 0.011 0.006 0.023 0.015 0.016 0.010 0.022 0.013 0.011 0.015 0.006 0.016 0.016 0.011 0.032 0.012 0.016 0.015 0.022 0.011 0.024 0.016 0.021 -0.025 0.006 -0.006
model_hashed_3 0.024 0.007 0.008 0.013 0.000 0.009 0.043 0.080 0.128 0.015 0.003 0.095 0.077 0.195 0.121 0.140 0.025 0.047 0.040 0.047 0.042 0.048 0.141 0.005 0.088 0.028 0.028 0.123 0.105 0.121 0.121 0.020 0.021 0.026 0.015 0.026 0.048 0.024 0.041 0.014 0.027 0.118 0.070 0.043 0.025 0.015 0.197 0.018 0.022 0.012 0.258 0.008 0.017 0.031 0.020 0.036 0.012 0.008 0.017 0.008 0.010 0.006 0.020 0.020 0.016 0.005 0.012 0.013 0.013 0.015 0.009 0.020 0.010 0.015 0.015 0.011 0.010 0.008 0.020 0.014 0.020 1.000 0.015 0.007 0.013 0.013 0.011 0.006 0.000 0.016 0.010 0.011 0.006 0.016 0.008 0.006 0.010 0.000 0.011 0.011 0.007 0.023 0.007 0.011 0.010 0.016 0.007 0.017 0.011 0.015 0.078 0.054 0.030
model_hashed_30 0.014 0.039 0.018 0.015 0.000 0.007 0.271 0.154 0.046 0.034 0.000 0.214 0.187 0.143 0.057 0.065 0.008 0.081 0.083 0.089 0.082 0.063 0.018 0.006 0.027 0.020 0.268 0.273 0.134 0.211 0.110 0.022 0.021 0.012 0.016 0.023 0.052 0.027 0.309 0.024 0.030 0.042 0.019 0.047 0.028 0.017 0.016 0.021 0.037 0.021 0.042 0.009 0.651 0.034 0.030 0.026 0.014 0.017 0.018 0.009 0.012 0.007 0.022 0.022 0.018 0.006 0.014 0.015 0.014 0.016 0.011 0.022 0.011 0.017 0.017 0.012 0.011 0.009 0.022 0.016 0.022 0.015 1.000 0.008 0.015 0.015 0.013 0.008 0.003 0.018 0.012 0.012 0.007 0.017 0.009 0.007 0.011 0.003 0.012 0.012 0.008 0.025 0.008 0.012 0.012 0.017 0.008 0.019 0.012 0.016 -0.041 0.062 0.029
model_hashed_31 0.006 0.004 0.007 0.251 0.000 0.000 0.016 0.044 0.000 0.000 0.000 0.076 0.053 0.105 0.069 0.078 0.007 0.031 0.035 0.040 0.017 0.030 0.019 0.000 0.029 0.029 0.052 0.064 0.071 0.068 0.054 0.247 0.021 0.060 0.008 0.016 0.031 0.015 0.030 0.054 0.017 0.024 0.010 0.027 0.016 0.010 0.045 0.314 0.016 0.024 0.025 0.000 0.010 0.019 0.032 0.023 0.006 0.000 0.009 0.000 0.004 0.000 0.012 0.012 0.009 0.000 0.006 0.007 0.006 0.008 0.003 0.012 0.003 0.008 0.008 0.004 0.003 0.000 0.012 0.008 0.011 0.007 0.008 1.000 0.006 0.007 0.005 0.000 0.000 0.009 0.004 0.004 0.000 0.008 0.000 0.000 0.003 0.000 0.004 0.004 0.000 0.014 0.000 0.004 0.004 0.008 0.000 0.010 0.004 0.008 0.040 0.025 -0.031
model_hashed_32 0.012 0.010 0.000 0.013 0.015 0.000 0.342 0.151 0.059 0.223 0.013 0.274 0.282 0.264 0.058 0.061 0.000 0.065 0.036 0.057 0.049 0.045 0.026 0.005 0.129 0.190 0.095 0.090 0.085 0.082 0.095 0.019 0.026 0.045 0.014 0.024 0.313 0.023 0.033 0.021 0.026 0.138 0.040 0.041 0.025 0.014 0.000 0.019 0.026 0.035 0.035 0.008 0.017 0.030 0.021 0.055 0.012 0.012 0.016 0.008 0.010 0.005 0.019 0.020 0.015 0.005 0.012 0.013 0.012 0.014 0.009 0.019 0.009 0.015 0.014 0.010 0.010 0.007 0.020 0.014 0.019 0.013 0.015 0.006 1.000 0.013 0.011 0.006 0.000 0.015 0.010 0.010 0.006 0.015 0.008 0.006 0.009 0.000 0.010 0.010 0.006 0.022 0.007 0.010 0.010 0.015 0.006 0.016 0.010 0.014 0.069 0.015 -0.007
model_hashed_33 0.013 0.010 0.025 0.013 0.000 0.006 0.044 0.082 0.052 0.005 0.045 0.121 0.101 0.089 0.065 0.054 0.029 0.072 0.106 0.065 0.052 0.045 0.015 0.005 0.068 0.099 0.031 0.077 0.051 0.041 0.053 0.020 0.014 0.012 0.015 0.025 0.048 0.024 0.046 0.044 0.027 0.037 0.017 0.042 0.025 0.015 0.038 0.019 0.026 0.040 0.038 0.008 0.017 0.030 0.027 0.445 0.200 0.000 0.016 0.008 0.010 0.005 0.020 0.020 0.016 0.005 0.012 0.013 0.012 0.014 0.009 0.020 0.009 0.015 0.015 0.010 0.010 0.008 0.020 0.014 0.019 0.013 0.015 0.007 0.013 1.000 0.011 0.006 0.000 0.016 0.010 0.010 0.006 0.015 0.008 0.006 0.010 0.000 0.010 0.010 0.006 0.022 0.007 0.010 0.010 0.015 0.007 0.017 0.010 0.014 0.043 0.037 -0.034
model_hashed_34 0.011 0.008 0.019 0.011 0.014 0.004 0.026 0.130 0.175 0.002 0.000 0.102 0.102 0.238 0.139 0.157 0.026 0.040 0.065 0.062 0.045 0.052 0.023 0.003 0.025 0.007 0.057 0.052 0.067 0.115 0.054 0.012 0.047 0.023 0.012 0.022 0.029 0.021 0.039 0.018 0.373 0.300 0.014 0.037 0.022 0.012 0.011 0.016 0.023 0.033 0.033 0.006 0.015 0.027 0.020 0.031 0.010 0.009 0.014 0.006 0.008 0.003 0.017 0.017 0.013 0.003 0.010 0.011 0.011 0.012 0.007 0.017 0.007 0.013 0.013 0.009 0.008 0.006 0.017 0.012 0.017 0.011 0.013 0.005 0.011 0.011 1.000 0.004 0.000 0.014 0.008 0.009 0.004 0.013 0.006 0.004 0.008 0.000 0.008 0.009 0.005 0.019 0.005 0.009 0.008 0.013 0.005 0.014 0.008 0.012 0.058 0.026 -0.007
model_hashed_35 0.006 0.003 0.041 0.006 0.000 0.000 0.027 0.045 0.053 0.046 0.000 0.149 0.073 0.111 0.036 0.035 0.020 0.040 0.017 0.024 0.020 0.027 0.056 0.000 0.030 0.016 0.094 0.053 0.047 0.037 0.047 0.011 0.011 0.119 0.007 0.015 0.029 0.014 0.028 0.042 0.016 0.016 0.009 0.026 0.062 0.165 0.109 0.100 0.015 0.053 0.023 0.000 0.009 0.018 0.016 0.022 0.005 0.000 0.009 0.000 0.003 0.000 0.011 0.011 0.008 0.000 0.005 0.006 0.006 0.007 0.001 0.011 0.002 0.008 0.007 0.004 0.003 0.000 0.011 0.007 0.011 0.006 0.008 0.000 0.006 0.006 0.004 1.000 0.000 0.008 0.003 0.003 0.000 0.008 0.000 0.000 0.002 0.000 0.003 0.003 0.000 0.013 0.000 0.004 0.003 0.008 0.000 0.009 0.003 0.007 -0.054 0.028 -0.019
model_hashed_36 0.000 0.079 0.028 0.000 0.000 0.000 0.020 0.055 0.062 0.000 0.000 0.088 0.095 0.079 0.015 0.033 0.061 0.036 0.029 0.048 0.053 0.018 0.011 0.000 0.012 0.027 0.048 0.033 0.034 0.036 0.048 0.007 0.274 0.023 0.002 0.117 0.015 0.009 0.021 0.008 0.011 0.017 0.005 0.019 0.010 0.003 0.007 0.006 0.011 0.023 0.017 0.000 0.005 0.013 0.023 0.016 0.000 0.000 0.004 0.000 0.000 0.000 0.007 0.007 0.004 0.000 0.000 0.000 0.000 0.002 0.000 0.007 0.000 0.003 0.003 0.000 0.000 0.000 0.007 0.002 0.006 0.000 0.003 0.000 0.000 0.000 0.000 0.000 1.000 0.004 0.000 0.000 0.000 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.008 0.000 0.000 0.000 0.003 0.000 0.005 0.000 0.002 -0.024 0.028 -0.021
model_hashed_37 0.015 0.008 0.109 0.008 0.012 0.008 0.046 0.085 0.073 0.007 0.005 0.174 0.178 0.122 0.139 0.106 0.086 0.053 0.076 0.057 0.092 0.053 0.026 0.015 0.027 0.023 0.086 0.111 0.086 0.074 0.087 0.023 0.031 0.025 0.015 0.024 0.302 0.028 0.053 0.025 0.379 0.041 0.019 0.154 0.029 0.018 0.023 0.022 0.031 0.044 0.045 0.010 0.020 0.036 0.031 0.041 0.015 0.018 0.019 0.010 0.013 0.007 0.023 0.024 0.019 0.007 0.015 0.016 0.015 0.017 0.012 0.023 0.012 0.018 0.018 0.013 0.012 0.010 0.024 0.017 0.023 0.016 0.018 0.009 0.015 0.016 0.014 0.008 0.004 1.000 0.012 0.013 0.008 0.018 0.010 0.008 0.012 0.004 0.013 0.013 0.009 0.026 0.009 0.013 0.013 0.018 0.009 0.020 0.013 0.017 0.045 0.044 -0.030
model_hashed_38 0.010 0.008 0.021 0.030 0.000 0.002 0.036 0.096 0.138 0.044 0.016 0.106 0.110 0.095 0.076 0.086 0.004 0.039 0.041 0.031 0.038 0.031 0.019 0.000 0.022 0.031 0.044 0.049 0.176 0.044 0.067 0.012 0.016 0.107 0.011 0.033 0.039 0.015 0.030 0.017 0.022 0.194 0.013 0.035 0.036 0.011 0.045 0.015 0.021 0.083 0.031 0.005 0.014 0.025 0.013 0.023 0.223 0.006 0.013 0.005 0.007 0.002 0.016 0.016 0.012 0.001 0.009 0.010 0.010 0.011 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.015 0.010 0.012 0.004 0.010 0.010 0.008 0.003 0.000 0.012 1.000 0.008 0.003 0.012 0.005 0.003 0.007 0.000 0.007 0.008 0.004 0.018 0.004 0.008 0.007 0.012 0.004 0.013 0.007 0.011 -0.081 0.026 -0.005
model_hashed_39 0.028 0.062 0.015 0.025 0.000 0.003 0.019 0.000 0.007 0.000 0.000 0.098 0.048 0.060 0.031 0.028 0.009 0.025 0.036 0.034 0.034 0.031 0.011 0.002 0.021 0.016 0.025 0.030 0.069 0.034 0.037 0.016 0.044 0.085 0.012 0.027 0.024 0.020 0.038 0.017 0.022 0.032 0.018 0.036 0.021 0.208 0.036 0.016 0.022 0.115 0.032 0.005 0.057 0.025 0.160 0.030 0.009 0.027 0.013 0.005 0.008 0.002 0.016 0.017 0.013 0.002 0.009 0.010 0.010 0.012 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.016 0.011 0.012 0.004 0.010 0.010 0.009 0.003 0.000 0.013 0.008 1.000 0.003 0.012 0.005 0.003 0.007 0.000 0.008 0.008 0.004 0.019 0.004 0.008 0.008 0.012 0.004 0.014 0.008 0.011 0.029 0.041 0.003
model_hashed_4 0.005 0.017 0.017 0.049 0.000 0.000 0.026 0.100 0.118 0.050 0.010 0.263 0.129 0.181 0.031 0.033 0.014 0.030 0.038 0.029 0.036 0.026 0.016 0.000 0.005 0.000 0.043 0.034 0.067 0.052 0.036 0.011 0.025 0.068 0.007 0.010 0.029 0.014 0.021 0.012 0.016 0.023 0.212 0.025 0.033 0.007 0.011 0.010 0.015 0.225 0.023 0.000 0.009 0.018 0.015 0.019 0.007 0.078 0.008 0.000 0.003 0.000 0.011 0.011 0.008 0.000 0.005 0.006 0.005 0.007 0.000 0.011 0.001 0.007 0.007 0.003 0.002 0.000 0.011 0.007 0.010 0.006 0.007 0.000 0.006 0.006 0.004 0.000 0.000 0.008 0.003 0.003 1.000 0.007 0.000 0.000 0.002 0.000 0.003 0.003 0.000 0.013 0.000 0.003 0.003 0.008 0.000 0.009 0.003 0.007 -0.028 0.061 0.006
model_hashed_40 0.015 0.012 0.027 0.015 0.000 0.007 0.049 0.084 0.075 0.007 0.002 0.092 0.044 0.099 0.046 0.054 0.034 0.059 0.055 0.045 0.070 0.035 0.029 0.007 0.025 0.060 0.077 0.082 0.085 0.088 0.083 0.022 0.014 0.023 0.017 0.028 0.136 0.027 0.024 0.024 0.049 0.042 0.331 0.048 0.350 0.017 0.022 0.022 0.046 0.037 0.043 0.010 0.020 0.071 0.028 0.040 0.012 0.000 0.019 0.010 0.012 0.007 0.022 0.023 0.018 0.007 0.014 0.015 0.015 0.017 0.011 0.022 0.011 0.017 0.017 0.012 0.012 0.009 0.023 0.016 0.022 0.016 0.017 0.008 0.015 0.015 0.013 0.008 0.003 0.018 0.012 0.012 0.007 1.000 0.010 0.008 0.012 0.003 0.012 0.012 0.008 0.026 0.009 0.012 0.012 0.018 0.008 0.019 0.012 0.016 -0.034 0.096 -0.083
model_hashed_41 0.002 0.000 0.010 0.002 0.000 0.000 0.021 0.026 0.009 0.000 0.047 0.094 0.064 0.067 0.031 0.025 0.014 0.120 0.033 0.044 0.031 0.163 0.012 0.000 0.020 0.012 0.068 0.057 0.046 0.048 0.047 0.008 0.012 0.008 0.004 0.325 0.023 0.010 0.000 0.009 0.012 0.018 0.006 0.020 0.011 0.004 0.008 0.007 0.012 0.018 0.018 0.000 0.006 0.000 0.012 0.017 0.001 0.008 0.011 0.002 0.005 0.000 0.013 0.013 0.010 0.000 0.007 0.008 0.007 0.009 0.004 0.013 0.004 0.009 0.009 0.006 0.005 0.002 0.013 0.009 0.013 0.008 0.009 0.000 0.008 0.008 0.006 0.000 0.000 0.010 0.005 0.005 0.000 0.010 1.000 0.000 0.005 0.000 0.005 0.005 0.000 0.015 0.000 0.006 0.005 0.010 0.000 0.011 0.005 0.009 0.048 0.012 0.018
model_hashed_42 0.005 0.003 0.012 0.092 0.000 0.000 0.027 0.029 0.017 0.000 0.000 0.120 0.042 0.147 0.029 0.029 0.008 0.031 0.035 0.029 0.033 0.031 0.010 0.000 0.009 0.016 0.032 0.051 0.052 0.027 0.040 0.011 0.015 0.018 0.007 0.015 0.075 0.014 0.028 0.012 0.016 0.023 0.024 0.103 0.015 0.075 0.056 0.010 0.139 0.022 0.023 0.000 0.009 0.018 0.026 0.021 0.005 0.005 0.008 0.000 0.003 0.000 0.011 0.011 0.008 0.000 0.005 0.006 0.005 0.007 0.001 0.011 0.001 0.007 0.007 0.003 0.002 0.000 0.011 0.007 0.011 0.006 0.007 0.000 0.006 0.006 0.004 0.000 0.000 0.008 0.003 0.003 0.000 0.008 0.000 1.000 0.002 0.000 0.003 0.003 0.000 0.013 0.000 0.003 0.003 0.008 0.000 0.009 0.003 0.007 -0.062 0.027 -0.018
model_hashed_43 0.009 0.007 0.006 0.010 0.000 0.002 0.035 0.076 0.052 0.000 0.000 0.118 0.111 0.076 0.059 0.056 0.013 0.077 0.088 0.090 0.091 0.056 0.021 0.000 0.066 0.091 0.042 0.047 0.056 0.058 0.100 0.015 0.095 0.026 0.011 0.020 0.038 0.019 0.028 0.017 0.021 0.030 0.087 0.034 0.020 0.011 0.015 0.015 0.590 0.030 0.023 0.005 0.013 0.024 0.021 0.029 0.009 0.015 0.013 0.005 0.007 0.000 0.015 0.016 0.012 0.000 0.009 0.010 0.009 0.011 0.006 0.015 0.006 0.011 0.011 0.007 0.007 0.004 0.016 0.011 0.015 0.010 0.011 0.003 0.009 0.010 0.008 0.002 0.000 0.012 0.007 0.007 0.002 0.012 0.005 0.002 1.000 0.000 0.007 0.007 0.003 0.018 0.004 0.007 0.007 0.012 0.003 0.013 0.007 0.011 0.094 0.079 -0.075
model_hashed_44 0.000 0.135 0.061 0.000 0.000 0.000 0.030 0.054 0.013 0.000 0.000 0.106 0.145 0.068 0.019 0.027 0.065 0.038 0.078 0.118 0.120 0.026 0.221 0.000 0.190 0.051 0.024 0.041 0.036 0.046 0.045 0.007 0.010 0.113 0.002 0.010 0.021 0.009 0.020 0.063 0.052 0.008 0.004 0.019 0.222 0.002 0.007 0.006 0.010 0.015 0.017 0.000 0.005 0.013 0.011 0.015 0.000 0.005 0.004 0.000 0.000 0.000 0.007 0.007 0.003 0.000 0.000 0.000 0.000 0.002 0.000 0.007 0.000 0.003 0.002 0.000 0.000 0.000 0.007 0.001 0.006 0.000 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.003 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.008 0.000 0.000 0.000 0.003 0.000 0.005 0.000 0.002 -0.001 0.049 0.045
model_hashed_45 0.010 0.000 0.016 0.007 0.000 0.003 0.032 0.144 0.196 0.000 0.000 0.094 0.079 0.250 0.156 0.163 0.015 0.026 0.056 0.048 0.035 0.045 0.015 0.001 0.029 0.022 0.029 0.071 0.057 0.062 0.043 0.016 0.021 0.021 0.012 0.021 0.035 0.020 0.038 0.017 0.022 0.031 0.013 0.051 0.185 0.012 0.033 0.012 0.022 0.032 0.195 0.400 0.014 0.025 0.022 0.030 0.041 0.000 0.013 0.005 0.008 0.002 0.016 0.016 0.013 0.001 0.009 0.010 0.010 0.012 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.016 0.011 0.012 0.004 0.010 0.010 0.008 0.003 0.000 0.013 0.007 0.008 0.003 0.012 0.005 0.003 0.007 0.000 1.000 0.008 0.004 0.018 0.004 0.008 0.007 0.012 0.004 0.014 0.008 0.011 0.116 0.053 -0.037
model_hashed_46 0.010 0.021 0.003 0.071 0.000 0.003 0.165 0.114 0.089 0.000 0.000 0.149 0.141 0.306 0.111 0.128 0.017 0.050 0.045 0.037 0.034 0.038 0.022 0.000 0.024 0.024 0.026 0.037 0.064 0.048 0.056 0.016 0.022 0.020 0.013 0.017 0.039 0.020 0.039 0.099 0.292 0.126 0.052 0.030 0.021 0.012 0.078 0.016 0.020 0.032 0.032 0.005 0.014 0.025 0.022 0.030 0.009 0.051 0.013 0.005 0.008 0.002 0.016 0.017 0.013 0.002 0.009 0.010 0.010 0.012 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.016 0.011 0.012 0.004 0.010 0.010 0.009 0.003 0.000 0.013 0.008 0.008 0.003 0.012 0.005 0.003 0.007 0.000 0.008 1.000 0.004 0.019 0.004 0.008 0.008 0.012 0.004 0.014 0.008 0.011 -0.059 0.070 -0.069
model_hashed_47 0.006 0.035 0.126 0.006 0.000 0.000 0.028 0.022 0.042 0.000 0.000 0.121 0.084 0.137 0.061 0.004 0.115 0.023 0.026 0.024 0.015 0.025 0.017 0.000 0.026 0.017 0.059 0.069 0.050 0.042 0.056 0.012 0.016 0.020 0.008 0.016 0.216 0.015 0.029 0.013 0.117 0.024 0.009 0.009 0.014 0.008 0.012 0.075 0.016 0.024 0.024 0.000 0.010 0.019 0.019 0.023 0.006 0.014 0.009 0.000 0.004 0.000 0.012 0.012 0.009 0.000 0.006 0.007 0.006 0.008 0.002 0.012 0.003 0.008 0.008 0.004 0.003 0.000 0.012 0.007 0.011 0.007 0.008 0.000 0.006 0.006 0.005 0.000 0.000 0.009 0.004 0.004 0.000 0.008 0.000 0.000 0.003 0.000 0.004 0.004 1.000 0.014 0.000 0.004 0.004 0.008 0.000 0.009 0.004 0.007 0.022 0.061 0.045
model_hashed_48 0.022 0.015 0.033 0.022 0.055 0.022 0.066 0.140 0.099 0.003 0.054 0.165 0.162 0.155 0.092 0.111 0.039 0.086 0.117 0.063 0.049 0.051 0.089 0.004 0.057 0.032 0.116 0.126 0.146 0.127 0.151 0.032 0.034 0.014 0.116 0.041 0.305 0.034 0.055 0.034 0.042 0.035 0.028 0.040 0.032 0.051 0.032 0.031 0.042 0.047 0.061 0.013 0.028 0.049 0.043 0.442 0.095 0.032 0.027 0.015 0.018 0.012 0.032 0.033 0.026 0.012 0.021 0.022 0.022 0.024 0.017 0.032 0.017 0.025 0.025 0.019 0.018 0.015 0.033 0.024 0.032 0.023 0.025 0.014 0.022 0.022 0.019 0.013 0.008 0.026 0.018 0.019 0.013 0.026 0.015 0.013 0.018 0.008 0.018 0.019 0.014 1.000 0.014 0.019 0.018 0.026 0.014 0.028 0.018 0.024 0.016 0.085 0.078
model_hashed_49 0.006 0.000 0.045 0.004 0.000 0.000 0.123 0.067 0.028 0.000 0.000 0.121 0.114 0.117 0.027 0.024 0.016 0.060 0.052 0.062 0.056 0.057 0.017 0.000 0.022 0.011 0.035 0.042 0.045 0.045 0.042 0.012 0.067 0.012 0.008 0.007 0.030 0.015 0.022 0.013 0.302 0.024 0.007 0.028 0.016 0.008 0.035 0.011 0.017 0.025 0.025 0.117 0.290 0.020 0.017 0.023 0.006 0.023 0.009 0.000 0.004 0.000 0.012 0.012 0.009 0.000 0.006 0.007 0.006 0.008 0.003 0.012 0.003 0.008 0.008 0.004 0.004 0.000 0.012 0.008 0.012 0.007 0.008 0.000 0.007 0.007 0.005 0.000 0.000 0.009 0.004 0.004 0.000 0.009 0.000 0.000 0.004 0.000 0.004 0.004 0.000 0.014 1.000 0.004 0.004 0.009 0.000 0.010 0.004 0.008 0.048 0.053 0.020
model_hashed_5 0.010 0.008 0.018 0.000 0.000 0.003 0.056 0.011 0.049 0.001 0.000 0.134 0.111 0.097 0.192 0.217 0.021 0.026 0.032 0.053 0.029 0.051 0.023 0.002 0.030 0.019 0.051 0.130 0.103 0.151 0.133 0.016 0.022 0.027 0.012 0.019 0.040 0.020 0.055 0.018 0.023 0.028 0.010 0.036 0.021 0.012 0.015 0.016 0.022 0.082 0.268 0.004 0.014 0.026 0.164 0.030 0.010 0.000 0.013 0.006 0.008 0.002 0.016 0.017 0.013 0.002 0.009 0.010 0.010 0.012 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.017 0.011 0.016 0.011 0.012 0.004 0.010 0.010 0.009 0.004 0.000 0.013 0.008 0.008 0.003 0.012 0.006 0.003 0.007 0.000 0.008 0.008 0.004 0.019 0.004 1.000 0.008 0.012 0.004 0.014 0.008 0.012 -0.141 0.015 0.007
model_hashed_50 0.010 0.014 0.007 0.010 0.000 0.003 0.036 0.080 0.087 0.017 0.000 0.121 0.115 0.125 0.031 0.035 0.012 0.037 0.038 0.041 0.097 0.054 0.018 0.017 0.026 0.033 0.120 0.119 0.103 0.094 0.112 0.016 0.166 0.029 0.011 0.139 0.039 0.019 0.038 0.428 0.022 0.031 0.007 0.035 0.020 0.011 0.019 0.015 0.021 0.074 0.031 0.002 0.014 0.025 0.022 0.029 0.009 0.016 0.013 0.005 0.007 0.002 0.016 0.016 0.012 0.001 0.009 0.010 0.010 0.011 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.015 0.010 0.012 0.004 0.010 0.010 0.008 0.003 0.000 0.013 0.007 0.008 0.003 0.012 0.005 0.003 0.007 0.000 0.007 0.008 0.004 0.018 0.004 0.008 1.000 0.012 0.004 0.013 0.007 0.011 0.052 0.075 0.022
model_hashed_51 0.015 0.064 0.009 0.088 0.000 0.008 0.041 0.058 0.056 0.006 0.000 0.161 0.106 0.071 0.059 0.060 0.027 0.125 0.048 0.056 0.048 0.071 0.218 0.007 0.135 0.026 0.046 0.083 0.101 0.089 0.053 0.023 0.094 0.032 0.017 0.231 0.054 0.027 0.052 0.018 0.031 0.043 0.158 0.048 0.029 0.131 0.017 0.021 0.400 0.033 0.094 0.010 0.020 0.035 0.026 0.030 0.014 0.007 0.019 0.010 0.012 0.007 0.023 0.023 0.018 0.007 0.014 0.015 0.015 0.017 0.011 0.022 0.011 0.017 0.017 0.012 0.012 0.009 0.023 0.016 0.022 0.016 0.017 0.008 0.015 0.015 0.013 0.008 0.003 0.018 0.012 0.012 0.008 0.018 0.010 0.008 0.012 0.003 0.012 0.012 0.008 0.026 0.009 0.012 0.012 1.000 0.008 0.019 0.012 0.017 -0.073 0.029 0.014
model_hashed_6 0.006 0.022 0.000 0.007 0.000 0.000 0.028 0.013 0.032 0.005 0.000 0.103 0.079 0.093 0.031 0.026 0.014 0.021 0.010 0.033 0.020 0.034 0.017 0.000 0.067 0.102 0.037 0.042 0.039 0.049 0.028 0.055 0.016 0.016 0.008 0.016 0.031 0.015 0.030 0.013 0.017 0.018 0.010 0.027 0.016 0.008 0.078 0.276 0.016 0.111 0.025 0.000 0.010 0.019 0.017 0.023 0.214 0.013 0.009 0.000 0.004 0.000 0.012 0.012 0.009 0.000 0.006 0.007 0.006 0.008 0.003 0.012 0.003 0.008 0.008 0.004 0.003 0.000 0.012 0.008 0.011 0.007 0.008 0.000 0.006 0.007 0.005 0.000 0.000 0.009 0.004 0.004 0.000 0.008 0.000 0.000 0.003 0.000 0.004 0.004 0.000 0.014 0.000 0.004 0.004 0.008 1.000 0.010 0.004 0.008 -0.003 0.034 0.011
model_hashed_7 0.444 0.013 0.217 0.009 0.000 0.104 0.031 0.186 0.136 0.019 0.000 0.378 0.153 0.277 0.196 0.134 0.271 0.061 0.066 0.047 0.054 0.061 0.246 0.008 0.154 0.022 0.055 0.079 0.061 0.074 0.079 0.025 0.045 0.040 0.019 0.031 0.058 0.214 0.133 0.023 0.033 0.046 0.021 0.052 0.031 0.019 0.025 0.024 0.032 0.189 0.047 0.011 0.022 0.037 0.047 0.275 0.016 0.000 0.021 0.011 0.013 0.008 0.025 0.025 0.020 0.008 0.016 0.017 0.016 0.018 0.012 0.024 0.012 0.019 0.019 0.014 0.013 0.011 0.025 0.018 0.024 0.017 0.019 0.010 0.016 0.017 0.014 0.009 0.005 0.020 0.013 0.014 0.009 0.019 0.011 0.009 0.013 0.005 0.014 0.014 0.009 0.028 0.010 0.014 0.013 0.019 0.010 1.000 0.014 0.018 0.113 0.068 0.020
model_hashed_8 0.000 0.109 0.089 0.157 0.000 0.003 0.034 0.054 0.046 0.000 0.000 0.151 0.079 0.102 0.034 0.027 0.051 0.121 0.030 0.060 0.022 0.123 0.022 0.000 0.032 0.022 0.077 0.048 0.048 0.039 0.037 0.016 0.021 0.126 0.012 0.408 0.040 0.020 0.038 0.017 0.022 0.031 0.013 0.035 0.021 0.012 0.016 0.015 0.022 0.058 0.032 0.005 0.000 0.070 0.022 0.030 0.009 0.013 0.013 0.005 0.008 0.002 0.016 0.016 0.013 0.001 0.009 0.010 0.010 0.012 0.007 0.016 0.007 0.012 0.012 0.008 0.007 0.005 0.016 0.011 0.016 0.011 0.012 0.004 0.010 0.010 0.008 0.003 0.000 0.013 0.007 0.008 0.003 0.012 0.005 0.003 0.007 0.000 0.008 0.008 0.004 0.018 0.004 0.008 0.007 0.012 0.004 0.014 1.000 0.011 0.014 0.040 0.033
model_hashed_9 0.014 0.011 0.019 0.014 0.000 0.007 0.047 0.086 0.093 0.006 0.004 0.116 0.064 0.091 0.065 0.057 0.029 0.061 0.050 0.065 0.039 0.068 0.029 0.006 0.089 0.148 0.069 0.068 0.099 0.096 0.055 0.021 0.028 0.027 0.016 0.027 0.051 0.026 0.046 0.023 0.029 0.168 0.018 0.042 0.027 0.016 0.018 0.019 0.185 0.038 0.041 0.009 0.018 0.355 0.022 0.038 0.013 0.015 0.018 0.009 0.011 0.006 0.021 0.022 0.017 0.006 0.013 0.014 0.014 0.016 0.010 0.021 0.010 0.016 0.016 0.012 0.011 0.009 0.021 0.015 0.021 0.015 0.016 0.008 0.014 0.014 0.012 0.007 0.002 0.017 0.011 0.011 0.007 0.016 0.009 0.007 0.011 0.002 0.011 0.011 0.007 0.024 0.008 0.012 0.011 0.017 0.008 0.018 0.011 1.000 0.019 0.040 0.040
msrp 0.078 0.043 0.102 -0.004 -0.002 0.014 0.170 0.013 -0.216 -0.026 -0.009 0.146 0.028 0.275 0.484 -0.565 0.088 0.005 0.063 -0.053 0.113 -0.012 0.198 -0.024 -0.203 0.098 0.004 -0.095 -0.059 0.080 -0.029 -0.025 0.098 0.125 -0.085 0.082 -0.148 0.048 0.011 0.014 -0.099 -0.144 0.029 0.135 -0.077 0.066 0.038 0.093 -0.095 0.285 -0.143 0.006 0.108 -0.054 -0.058 -0.114 0.031 -0.263 0.097 0.035 0.033 0.008 0.016 0.080 -0.025 0.007 0.026 0.011 0.009 -0.038 -0.016 0.069 -0.030 0.027 -0.032 -0.023 0.006 -0.014 -0.031 0.008 -0.025 0.078 -0.041 0.040 0.069 0.043 0.058 -0.054 -0.024 0.045 -0.081 0.029 -0.028 -0.034 0.048 -0.062 0.094 -0.001 0.116 -0.059 0.022 0.016 0.048 -0.141 0.052 -0.073 -0.003 0.113 0.014 0.019 1.000 0.210 0.313
stock_type 0.026 0.054 0.114 0.004 0.019 0.008 0.017 0.163 0.154 0.055 0.000 0.217 0.174 0.170 0.123 0.090 0.084 0.118 0.102 0.119 0.096 0.119 0.119 0.066 0.116 0.067 0.078 0.111 0.103 0.056 0.087 0.048 0.079 0.104 0.036 0.004 0.060 0.033 0.028 0.044 0.072 0.112 0.012 0.052 0.061 0.032 0.068 0.044 0.091 0.027 0.043 0.082 0.019 0.040 0.093 0.094 0.030 0.282 0.086 0.035 0.054 0.034 0.056 0.048 0.030 0.067 0.038 0.052 0.036 0.000 0.010 0.098 0.000 0.048 0.071 0.029 0.032 0.075 0.072 0.026 0.006 0.054 0.062 0.025 0.015 0.037 0.026 0.028 0.028 0.044 0.026 0.041 0.061 0.096 0.012 0.027 0.079 0.049 0.053 0.070 0.061 0.085 0.053 0.015 0.075 0.029 0.034 0.068 0.040 0.040 0.210 1.000 0.882
year 0.023 -0.056 -0.148 -0.004 0.016 -0.007 0.023 0.186 -0.163 -0.076 -0.013 -0.027 0.003 -0.033 0.164 -0.108 -0.133 0.033 -0.002 -0.023 -0.034 -0.020 0.088 -0.094 -0.090 0.071 0.043 -0.064 -0.041 0.012 -0.016 -0.032 -0.074 -0.050 -0.049 0.002 -0.049 -0.004 -0.022 -0.036 -0.057 0.103 -0.024 0.031 0.054 -0.025 -0.067 0.016 0.093 -0.010 0.038 -0.050 0.051 0.060 -0.089 0.087 -0.017 -0.859 -0.004 0.031 0.043 -0.011 -0.033 -0.018 0.025 0.044 -0.022 -0.066 0.039 -0.001 -0.009 0.072 -0.012 -0.050 -0.064 0.015 -0.031 -0.065 0.063 0.015 -0.006 0.030 0.029 -0.031 -0.007 -0.034 -0.007 -0.019 -0.021 -0.030 -0.005 0.003 0.006 -0.083 0.018 -0.018 -0.075 0.045 -0.037 -0.069 0.045 0.078 0.020 0.007 0.022 0.014 0.011 0.020 0.033 0.040 0.313 0.882 1.000

Missing values

2024-05-20T00:01:54.030577 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-20T00:01:54.995292 image/svg+xml Matplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

msrp year mileage stock_type model_hashed_0 model_hashed_1 model_hashed_2 model_hashed_3 model_hashed_4 model_hashed_5 model_hashed_6 model_hashed_7 model_hashed_8 model_hashed_9 model_hashed_10 model_hashed_11 model_hashed_12 model_hashed_13 model_hashed_14 model_hashed_15 model_hashed_16 model_hashed_17 model_hashed_18 model_hashed_19 model_hashed_20 model_hashed_21 model_hashed_22 model_hashed_23 model_hashed_24 model_hashed_25 model_hashed_26 model_hashed_27 model_hashed_28 model_hashed_29 model_hashed_30 model_hashed_31 model_hashed_32 model_hashed_33 model_hashed_34 model_hashed_35 model_hashed_36 model_hashed_37 model_hashed_38 model_hashed_39 model_hashed_40 model_hashed_41 model_hashed_42 model_hashed_43 model_hashed_44 model_hashed_45 model_hashed_46 model_hashed_47 model_hashed_48 model_hashed_49 model_hashed_50 model_hashed_51 exterior_color_x0 exterior_color_x1 exterior_color_x2 exterior_color_x3 exterior_color_x4 interior_color_x0 interior_color_x1 interior_color_x2 interior_color_x3 interior_color_x4 drivetrain_All-wheel Drive drivetrain_Front-wheel Drive drivetrain_Rear-wheel Drive make_Acura make_Audi make_BMW make_Buick make_Cadillac make_Chevrolet make_Dodge make_Ford make_GMC make_Honda make_Hyundai make_INFINITI make_Jeep make_Kia make_Land Rover make_Lexus make_Lincoln make_Mazda make_Mercedes-Benz make_Nissan make_Porsche make_RAM make_Subaru make_Toyota make_Volkswagen make_Volvo bodystyle_Cargo Van bodystyle_Convertible bodystyle_Coupe bodystyle_Hatchback bodystyle_Minivan bodystyle_Passenger Van bodystyle_Pickup Truck bodystyle_SUV bodystyle_Sedan bodystyle_Wagon bodystyle_nan cat_x0 cat_x1 cat_x2 fuel_type_Electric fuel_type_Flexible fuel_type_Gasoline fuel_type_Hybrid
0 57215.0 2024.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.016743 0.079723 0.957974 -0.333534 -0.814538 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.049847 -0.299780 0.846171 1.0 0.0 0.0 0.0
1 27995.0 2024.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 1.256852 0.710646 0.237572 0.141691 -1.756619 -0.608476 0.728257 0.914057 -0.787633 0.176680 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.012299 -0.232188 0.889274 0.0 0.0 1.0 0.0
2 83630.0 2024.0 20.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.194003 0.349930 0.440473 -0.011486 -1.480162 -0.609001 0.757246 0.397062 -0.283482 0.090835 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.049847 -0.299780 0.846171 1.0 0.0 0.0 0.0
3 50185.0 2024.0 16.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.061446 -0.098841 1.611720 -2.235823 -0.691968 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.463199 -0.303016 1.513099 0.0 0.0 0.0 1.0
4 27825.0 2024.0 6.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.438918 1.269402 -0.225778 -1.236302 -2.456789 -0.331483 0.228954 0.356856 -0.326739 0.014822 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0
5 53727.0 2024.0 10.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.433999 0.108471 1.129237 -0.246424 -0.847668 -0.714561 0.834606 1.041396 -0.241518 -0.057818 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.401217 -0.237615 0.750374 0.0 0.0 1.0 0.0
6 68365.0 2024.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.751181 1.597053 1.300163 -0.063247 -0.428936 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.463199 -0.303016 1.513099 0.0 0.0 0.0 1.0
7 64305.0 2024.0 15.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.180491 1.018271 0.178789 0.537705 -1.773561 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.463199 -0.303016 1.513099 0.0 0.0 0.0 1.0
8 83630.0 2024.0 16.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.898154 0.828910 1.027585 -0.377348 -0.477066 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.049847 -0.299780 0.846171 1.0 0.0 0.0 0.0
9 34752.0 2023.0 1332.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.888035 0.735458 1.399905 -0.610465 -0.496613 0.146235 0.101405 0.135154 0.015257 0.127018 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0
msrp year mileage stock_type model_hashed_0 model_hashed_1 model_hashed_2 model_hashed_3 model_hashed_4 model_hashed_5 model_hashed_6 model_hashed_7 model_hashed_8 model_hashed_9 model_hashed_10 model_hashed_11 model_hashed_12 model_hashed_13 model_hashed_14 model_hashed_15 model_hashed_16 model_hashed_17 model_hashed_18 model_hashed_19 model_hashed_20 model_hashed_21 model_hashed_22 model_hashed_23 model_hashed_24 model_hashed_25 model_hashed_26 model_hashed_27 model_hashed_28 model_hashed_29 model_hashed_30 model_hashed_31 model_hashed_32 model_hashed_33 model_hashed_34 model_hashed_35 model_hashed_36 model_hashed_37 model_hashed_38 model_hashed_39 model_hashed_40 model_hashed_41 model_hashed_42 model_hashed_43 model_hashed_44 model_hashed_45 model_hashed_46 model_hashed_47 model_hashed_48 model_hashed_49 model_hashed_50 model_hashed_51 exterior_color_x0 exterior_color_x1 exterior_color_x2 exterior_color_x3 exterior_color_x4 interior_color_x0 interior_color_x1 interior_color_x2 interior_color_x3 interior_color_x4 drivetrain_All-wheel Drive drivetrain_Front-wheel Drive drivetrain_Rear-wheel Drive make_Acura make_Audi make_BMW make_Buick make_Cadillac make_Chevrolet make_Dodge make_Ford make_GMC make_Honda make_Hyundai make_INFINITI make_Jeep make_Kia make_Land Rover make_Lexus make_Lincoln make_Mazda make_Mercedes-Benz make_Nissan make_Porsche make_RAM make_Subaru make_Toyota make_Volkswagen make_Volvo bodystyle_Cargo Van bodystyle_Convertible bodystyle_Coupe bodystyle_Hatchback bodystyle_Minivan bodystyle_Passenger Van bodystyle_Pickup Truck bodystyle_SUV bodystyle_Sedan bodystyle_Wagon bodystyle_nan cat_x0 cat_x1 cat_x2 fuel_type_Electric fuel_type_Flexible fuel_type_Gasoline fuel_type_Hybrid
37596 28560.00 2024.0 27.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.787154 -0.143681 1.208829 -0.475173 -1.336256 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0
37597 51721.00 2024.0 5.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.095597 0.431247 0.584988 -0.792408 -0.239588 -0.302017 0.271770 0.257392 0.001024 -0.100778 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.401217 -0.237615 0.750374 0.0 0.0 1.0 0.0
37598 34420.00 2024.0 6.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.290412 1.251814 0.154810 -0.992802 -1.668267 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0
37599 34255.00 2024.0 5.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.916740 0.668999 0.539366 0.067219 -1.353132 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0
37600 51195.00 2023.0 5.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.140053 0.293042 0.583253 -1.187837 -0.515819 -0.292383 0.332437 0.257222 0.096939 -0.151998 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.225831 -0.303398 0.753570 1.0 0.0 0.0 0.0
37601 36669.00 2024.0 6.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.492131 0.861728 0.731555 -0.539116 -0.660716 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0
37602 51443.00 2024.0 7.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.433999 0.108471 1.129237 -0.246424 -0.847668 -1.012676 0.687068 0.476377 -0.351794 0.587591 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.401217 -0.237615 0.750374 0.0 0.0 1.0 0.0
37603 50007.31 2022.0 17815.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.277456 0.276296 0.920459 -0.063368 -1.298279 -0.355613 0.302936 0.150128 0.110641 -0.217025 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.401217 -0.237615 0.750374 0.0 0.0 1.0 0.0
37604 57135.00 2024.0 14.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.560629 0.547981 0.728140 -0.012073 -1.663149 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.222460 -0.613471 0.918163 0.0 0.0 1.0 0.0
37605 75280.00 2024.0 3.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.751181 1.597053 1.300163 -0.063247 -0.428936 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.335627 -0.261183 1.265678 0.0 0.0 1.0 0.0

Duplicate rows

Most frequently occurring

msrp year mileage stock_type model_hashed_0 model_hashed_1 model_hashed_2 model_hashed_3 model_hashed_4 model_hashed_5 model_hashed_6 model_hashed_7 model_hashed_8 model_hashed_9 model_hashed_10 model_hashed_11 model_hashed_12 model_hashed_13 model_hashed_14 model_hashed_15 model_hashed_16 model_hashed_17 model_hashed_18 model_hashed_19 model_hashed_20 model_hashed_21 model_hashed_22 model_hashed_23 model_hashed_24 model_hashed_25 model_hashed_26 model_hashed_27 model_hashed_28 model_hashed_29 model_hashed_30 model_hashed_31 model_hashed_32 model_hashed_33 model_hashed_34 model_hashed_35 model_hashed_36 model_hashed_37 model_hashed_38 model_hashed_39 model_hashed_40 model_hashed_41 model_hashed_42 model_hashed_43 model_hashed_44 model_hashed_45 model_hashed_46 model_hashed_47 model_hashed_48 model_hashed_49 model_hashed_50 model_hashed_51 exterior_color_x0 exterior_color_x1 exterior_color_x2 exterior_color_x3 exterior_color_x4 interior_color_x0 interior_color_x1 interior_color_x2 interior_color_x3 interior_color_x4 drivetrain_All-wheel Drive drivetrain_Front-wheel Drive drivetrain_Rear-wheel Drive make_Acura make_Audi make_BMW make_Buick make_Cadillac make_Chevrolet make_Dodge make_Ford make_GMC make_Honda make_Hyundai make_INFINITI make_Jeep make_Kia make_Land Rover make_Lexus make_Lincoln make_Mazda make_Mercedes-Benz make_Nissan make_Porsche make_RAM make_Subaru make_Toyota make_Volkswagen make_Volvo bodystyle_Cargo Van bodystyle_Convertible bodystyle_Coupe bodystyle_Hatchback bodystyle_Minivan bodystyle_Passenger Van bodystyle_Pickup Truck bodystyle_SUV bodystyle_Sedan bodystyle_Wagon bodystyle_nan cat_x0 cat_x1 cat_x2 fuel_type_Electric fuel_type_Flexible fuel_type_Gasoline fuel_type_Hybrid # duplicates
104 18670.336429 2010.0 30863.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.120363 0.751485 0.935431 -0.592282 -0.530580 -0.714182 0.739052 1.171816 -0.371113 -0.029520 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0 6
2203 45009.358095 2020.0 41725.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.531811 -0.029267 1.072996 -0.207851 -0.945793 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.016687 -0.866967 1.471661 0.0 0.0 1.0 0.0 6
2506 49081.616667 2020.0 31150.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.570881 0.349454 0.686546 -0.185022 -0.751815 -0.313953 0.548013 0.323879 0.080760 -0.194509 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.830566 -0.789087 1.447463 0.0 0.0 1.0 0.0 6
2686 51175.000000 2024.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.916740 0.668999 0.539366 0.067219 -1.353132 -0.355613 0.302936 0.150128 0.110641 -0.217025 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.401217 -0.237615 0.750374 0.0 0.0 1.0 0.0 6
3457 73245.515238 2012.0 80134.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.051925 1.393476 0.185694 -1.096488 -1.048945 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.166069 0.307771 -0.271931 0.0 0.0 1.0 0.0 6
3654 102453.800000 2022.0 5622.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.850547 0.871684 0.122104 -0.693916 -1.209294 -0.613360 0.510440 0.777736 -0.306191 -0.079545 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.830566 -0.789087 1.447463 0.0 0.0 1.0 0.0 6
81 15369.490000 2015.0 53164.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.751181 1.597053 1.300163 -0.063247 -0.428936 -0.417855 0.405507 0.274520 0.082601 -0.089039 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.767909 -1.809606 2.565698 0.0 0.0 1.0 0.0 5
290 23012.173333 2018.0 41266.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.862309 0.192830 0.632283 -0.030009 -0.538724 -0.313953 0.548013 0.323879 0.080760 -0.194509 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 -0.012299 -0.232188 0.889274 0.0 0.0 1.0 0.0 5
513 25876.939286 2021.0 28564.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.649163 0.417381 1.318107 -1.045119 -0.510660 -0.313953 0.548013 0.323879 0.080760 -0.194509 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0 5
906 30005.000000 2024.0 10.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.183522 0.802371 0.581440 -0.406558 -1.325146 -0.494265 0.387535 0.589787 0.338319 -0.498702 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.123281 -0.381605 0.743733 0.0 0.0 1.0 0.0 5